fuzzy-rules-generator/turbine_regression_co.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>AP</th>\n",
       "      <th>AH</th>\n",
       "      <th>AFDP</th>\n",
       "      <th>GTEP</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "      <th>TEY</th>\n",
       "      <th>CDP</th>\n",
       "      <th>CO</th>\n",
       "      <th>NOX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.5878</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>83.675</td>\n",
       "      <td>3.5758</td>\n",
       "      <td>23.979</td>\n",
       "      <td>1086.2</td>\n",
       "      <td>549.83</td>\n",
       "      <td>134.67</td>\n",
       "      <td>11.898</td>\n",
       "      <td>0.32663</td>\n",
       "      <td>81.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.2932</td>\n",
       "      <td>1018.3</td>\n",
       "      <td>84.235</td>\n",
       "      <td>3.5709</td>\n",
       "      <td>23.951</td>\n",
       "      <td>1086.1</td>\n",
       "      <td>550.05</td>\n",
       "      <td>134.67</td>\n",
       "      <td>11.892</td>\n",
       "      <td>0.44784</td>\n",
       "      <td>82.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.9045</td>\n",
       "      <td>1018.4</td>\n",
       "      <td>84.858</td>\n",
       "      <td>3.5828</td>\n",
       "      <td>23.990</td>\n",
       "      <td>1086.5</td>\n",
       "      <td>550.19</td>\n",
       "      <td>135.10</td>\n",
       "      <td>12.042</td>\n",
       "      <td>0.45144</td>\n",
       "      <td>83.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7436</td>\n",
       "      <td>1018.3</td>\n",
       "      <td>85.434</td>\n",
       "      <td>3.5808</td>\n",
       "      <td>23.911</td>\n",
       "      <td>1086.5</td>\n",
       "      <td>550.17</td>\n",
       "      <td>135.03</td>\n",
       "      <td>11.990</td>\n",
       "      <td>0.23107</td>\n",
       "      <td>82.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.7516</td>\n",
       "      <td>1017.8</td>\n",
       "      <td>85.182</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>23.917</td>\n",
       "      <td>1085.9</td>\n",
       "      <td>550.00</td>\n",
       "      <td>134.67</td>\n",
       "      <td>11.910</td>\n",
       "      <td>0.26747</td>\n",
       "      <td>82.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36729</th>\n",
       "      <td>3.6268</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>93.200</td>\n",
       "      <td>3.1661</td>\n",
       "      <td>19.087</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>541.59</td>\n",
       "      <td>109.08</td>\n",
       "      <td>10.411</td>\n",
       "      <td>10.99300</td>\n",
       "      <td>89.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36730</th>\n",
       "      <td>4.1674</td>\n",
       "      <td>1028.6</td>\n",
       "      <td>94.036</td>\n",
       "      <td>3.1923</td>\n",
       "      <td>19.016</td>\n",
       "      <td>1037.6</td>\n",
       "      <td>542.28</td>\n",
       "      <td>108.79</td>\n",
       "      <td>10.344</td>\n",
       "      <td>11.14400</td>\n",
       "      <td>88.849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36731</th>\n",
       "      <td>5.4820</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>95.219</td>\n",
       "      <td>3.3128</td>\n",
       "      <td>18.857</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>543.48</td>\n",
       "      <td>107.81</td>\n",
       "      <td>10.462</td>\n",
       "      <td>11.41400</td>\n",
       "      <td>96.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36732</th>\n",
       "      <td>5.8837</td>\n",
       "      <td>1028.7</td>\n",
       "      <td>94.200</td>\n",
       "      <td>3.9831</td>\n",
       "      <td>23.563</td>\n",
       "      <td>1076.9</td>\n",
       "      <td>550.11</td>\n",
       "      <td>131.41</td>\n",
       "      <td>11.771</td>\n",
       "      <td>3.31340</td>\n",
       "      <td>64.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36733</th>\n",
       "      <td>6.0392</td>\n",
       "      <td>1028.8</td>\n",
       "      <td>94.547</td>\n",
       "      <td>3.8752</td>\n",
       "      <td>22.524</td>\n",
       "      <td>1067.9</td>\n",
       "      <td>548.23</td>\n",
       "      <td>125.41</td>\n",
       "      <td>11.462</td>\n",
       "      <td>11.98100</td>\n",
       "      <td>109.240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36733 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AT      AP      AH    AFDP    GTEP     TIT     TAT     TEY     CDP  \\\n",
       "1      4.5878  1018.7  83.675  3.5758  23.979  1086.2  549.83  134.67  11.898   \n",
       "2      4.2932  1018.3  84.235  3.5709  23.951  1086.1  550.05  134.67  11.892   \n",
       "3      3.9045  1018.4  84.858  3.5828  23.990  1086.5  550.19  135.10  12.042   \n",
       "4      3.7436  1018.3  85.434  3.5808  23.911  1086.5  550.17  135.03  11.990   \n",
       "5      3.7516  1017.8  85.182  3.5781  23.917  1085.9  550.00  134.67  11.910   \n",
       "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "36729  3.6268  1028.5  93.200  3.1661  19.087  1037.0  541.59  109.08  10.411   \n",
       "36730  4.1674  1028.6  94.036  3.1923  19.016  1037.6  542.28  108.79  10.344   \n",
       "36731  5.4820  1028.5  95.219  3.3128  18.857  1038.0  543.48  107.81  10.462   \n",
       "36732  5.8837  1028.7  94.200  3.9831  23.563  1076.9  550.11  131.41  11.771   \n",
       "36733  6.0392  1028.8  94.547  3.8752  22.524  1067.9  548.23  125.41  11.462   \n",
       "\n",
       "             CO      NOX  \n",
       "1       0.32663   81.952  \n",
       "2       0.44784   82.377  \n",
       "3       0.45144   83.776  \n",
       "4       0.23107   82.505  \n",
       "5       0.26747   82.028  \n",
       "...         ...      ...  \n",
       "36729  10.99300   89.172  \n",
       "36730  11.14400   88.849  \n",
       "36731  11.41400   96.147  \n",
       "36732   3.31340   64.738  \n",
       "36733  11.98100  109.240  \n",
       "\n",
       "[36733 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"data-turbine/gt_full.csv\", index_col=0)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>17.712726</td>\n",
       "      <td>7.447451</td>\n",
       "      <td>-6.234800</td>\n",
       "      <td>11.7810</td>\n",
       "      <td>17.8010</td>\n",
       "      <td>23.6650</td>\n",
       "      <td>37.1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>1013.070165</td>\n",
       "      <td>6.463346</td>\n",
       "      <td>985.850000</td>\n",
       "      <td>1008.8000</td>\n",
       "      <td>1012.6000</td>\n",
       "      <td>1017.0000</td>\n",
       "      <td>1036.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AH</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>77.867015</td>\n",
       "      <td>14.461355</td>\n",
       "      <td>24.085000</td>\n",
       "      <td>68.1880</td>\n",
       "      <td>80.4700</td>\n",
       "      <td>89.3760</td>\n",
       "      <td>100.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFDP</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>3.925518</td>\n",
       "      <td>0.773936</td>\n",
       "      <td>2.087400</td>\n",
       "      <td>3.3556</td>\n",
       "      <td>3.9377</td>\n",
       "      <td>4.3769</td>\n",
       "      <td>7.6106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEP</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>25.563801</td>\n",
       "      <td>4.195957</td>\n",
       "      <td>17.698000</td>\n",
       "      <td>23.1290</td>\n",
       "      <td>25.1040</td>\n",
       "      <td>29.0610</td>\n",
       "      <td>40.7160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIT</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>1081.428084</td>\n",
       "      <td>17.536373</td>\n",
       "      <td>1000.800000</td>\n",
       "      <td>1071.8000</td>\n",
       "      <td>1085.9000</td>\n",
       "      <td>1097.0000</td>\n",
       "      <td>1100.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAT</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>546.158517</td>\n",
       "      <td>6.842360</td>\n",
       "      <td>511.040000</td>\n",
       "      <td>544.7200</td>\n",
       "      <td>549.8800</td>\n",
       "      <td>550.0400</td>\n",
       "      <td>550.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEY</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>133.506404</td>\n",
       "      <td>15.618634</td>\n",
       "      <td>100.020000</td>\n",
       "      <td>124.4500</td>\n",
       "      <td>133.7300</td>\n",
       "      <td>144.0800</td>\n",
       "      <td>179.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDP</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>12.060525</td>\n",
       "      <td>1.088795</td>\n",
       "      <td>9.851800</td>\n",
       "      <td>11.4350</td>\n",
       "      <td>11.9650</td>\n",
       "      <td>12.8550</td>\n",
       "      <td>15.1590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>2.372468</td>\n",
       "      <td>2.262672</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>1.1824</td>\n",
       "      <td>1.7135</td>\n",
       "      <td>2.8429</td>\n",
       "      <td>44.1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>65.293067</td>\n",
       "      <td>11.678357</td>\n",
       "      <td>25.905000</td>\n",
       "      <td>57.1620</td>\n",
       "      <td>63.8490</td>\n",
       "      <td>71.5480</td>\n",
       "      <td>119.9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count         mean        std          min        25%        50%  \\\n",
       "AT    36733.0    17.712726   7.447451    -6.234800    11.7810    17.8010   \n",
       "AP    36733.0  1013.070165   6.463346   985.850000  1008.8000  1012.6000   \n",
       "AH    36733.0    77.867015  14.461355    24.085000    68.1880    80.4700   \n",
       "AFDP  36733.0     3.925518   0.773936     2.087400     3.3556     3.9377   \n",
       "GTEP  36733.0    25.563801   4.195957    17.698000    23.1290    25.1040   \n",
       "TIT   36733.0  1081.428084  17.536373  1000.800000  1071.8000  1085.9000   \n",
       "TAT   36733.0   546.158517   6.842360   511.040000   544.7200   549.8800   \n",
       "TEY   36733.0   133.506404  15.618634   100.020000   124.4500   133.7300   \n",
       "CDP   36733.0    12.060525   1.088795     9.851800    11.4350    11.9650   \n",
       "CO    36733.0     2.372468   2.262672     0.000388     1.1824     1.7135   \n",
       "NOX   36733.0    65.293067  11.678357    25.905000    57.1620    63.8490   \n",
       "\n",
       "            75%        max  \n",
       "AT      23.6650    37.1030  \n",
       "AP    1017.0000  1036.6000  \n",
       "AH      89.3760   100.2000  \n",
       "AFDP     4.3769     7.6106  \n",
       "GTEP    29.0610    40.7160  \n",
       "TIT   1097.0000  1100.9000  \n",
       "TAT    550.0400   550.6100  \n",
       "TEY    144.0800   179.5000  \n",
       "CDP     12.8550    15.1590  \n",
       "CO       2.8429    44.1030  \n",
       "NOX     71.5480   119.9100  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(data.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>AP</th>\n",
       "      <th>AH</th>\n",
       "      <th>AFDP</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "      <th>CO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.5878</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>83.675</td>\n",
       "      <td>3.5758</td>\n",
       "      <td>1086.2</td>\n",
       "      <td>549.83</td>\n",
       "      <td>0.32663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.2932</td>\n",
       "      <td>1018.3</td>\n",
       "      <td>84.235</td>\n",
       "      <td>3.5709</td>\n",
       "      <td>1086.1</td>\n",
       "      <td>550.05</td>\n",
       "      <td>0.44784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.9045</td>\n",
       "      <td>1018.4</td>\n",
       "      <td>84.858</td>\n",
       "      <td>3.5828</td>\n",
       "      <td>1086.5</td>\n",
       "      <td>550.19</td>\n",
       "      <td>0.45144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7436</td>\n",
       "      <td>1018.3</td>\n",
       "      <td>85.434</td>\n",
       "      <td>3.5808</td>\n",
       "      <td>1086.5</td>\n",
       "      <td>550.17</td>\n",
       "      <td>0.23107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.7516</td>\n",
       "      <td>1017.8</td>\n",
       "      <td>85.182</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>1085.9</td>\n",
       "      <td>550.00</td>\n",
       "      <td>0.26747</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>36729</th>\n",
       "      <td>3.6268</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>93.200</td>\n",
       "      <td>3.1661</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>541.59</td>\n",
       "      <td>10.99300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36730</th>\n",
       "      <td>4.1674</td>\n",
       "      <td>1028.6</td>\n",
       "      <td>94.036</td>\n",
       "      <td>3.1923</td>\n",
       "      <td>1037.6</td>\n",
       "      <td>542.28</td>\n",
       "      <td>11.14400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36731</th>\n",
       "      <td>5.4820</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>95.219</td>\n",
       "      <td>3.3128</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>543.48</td>\n",
       "      <td>11.41400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36732</th>\n",
       "      <td>5.8837</td>\n",
       "      <td>1028.7</td>\n",
       "      <td>94.200</td>\n",
       "      <td>3.9831</td>\n",
       "      <td>1076.9</td>\n",
       "      <td>550.11</td>\n",
       "      <td>3.31340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36733</th>\n",
       "      <td>6.0392</td>\n",
       "      <td>1028.8</td>\n",
       "      <td>94.547</td>\n",
       "      <td>3.8752</td>\n",
       "      <td>1067.9</td>\n",
       "      <td>548.23</td>\n",
       "      <td>11.98100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36733 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AT      AP      AH    AFDP     TIT     TAT        CO\n",
       "1      4.5878  1018.7  83.675  3.5758  1086.2  549.83   0.32663\n",
       "2      4.2932  1018.3  84.235  3.5709  1086.1  550.05   0.44784\n",
       "3      3.9045  1018.4  84.858  3.5828  1086.5  550.19   0.45144\n",
       "4      3.7436  1018.3  85.434  3.5808  1086.5  550.17   0.23107\n",
       "5      3.7516  1017.8  85.182  3.5781  1085.9  550.00   0.26747\n",
       "...       ...     ...     ...     ...     ...     ...       ...\n",
       "36729  3.6268  1028.5  93.200  3.1661  1037.0  541.59  10.99300\n",
       "36730  4.1674  1028.6  94.036  3.1923  1037.6  542.28  11.14400\n",
       "36731  5.4820  1028.5  95.219  3.3128  1038.0  543.48  11.41400\n",
       "36732  5.8837  1028.7  94.200  3.9831  1076.9  550.11   3.31340\n",
       "36733  6.0392  1028.8  94.547  3.8752  1067.9  548.23  11.98100\n",
       "\n",
       "[36733 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\"GTEP\", \"TEY\", \"CDP\", \"NOX\"], axis=1, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AT</th>\n",
       "      <th>AP</th>\n",
       "      <th>AH</th>\n",
       "      <th>AFDP</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4481</th>\n",
       "      <td>26.6350</td>\n",
       "      <td>1009.7</td>\n",
       "      <td>83.256</td>\n",
       "      <td>4.4137</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>540.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24884</th>\n",
       "      <td>20.2280</td>\n",
       "      <td>1016.2</td>\n",
       "      <td>73.583</td>\n",
       "      <td>4.6238</td>\n",
       "      <td>1099.8</td>\n",
       "      <td>538.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21558</th>\n",
       "      <td>15.6330</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>81.089</td>\n",
       "      <td>4.0899</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>16.6540</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>64.757</td>\n",
       "      <td>4.5755</td>\n",
       "      <td>1086.6</td>\n",
       "      <td>549.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21389</th>\n",
       "      <td>21.0020</td>\n",
       "      <td>1004.3</td>\n",
       "      <td>75.645</td>\n",
       "      <td>4.1101</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25726</th>\n",
       "      <td>17.5190</td>\n",
       "      <td>1015.9</td>\n",
       "      <td>85.663</td>\n",
       "      <td>3.6809</td>\n",
       "      <td>1072.2</td>\n",
       "      <td>549.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>21.9780</td>\n",
       "      <td>1014.4</td>\n",
       "      <td>75.280</td>\n",
       "      <td>3.1246</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>549.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22585</th>\n",
       "      <td>4.7103</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>92.874</td>\n",
       "      <td>3.2741</td>\n",
       "      <td>1067.2</td>\n",
       "      <td>550.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>6.7758</td>\n",
       "      <td>1008.3</td>\n",
       "      <td>93.029</td>\n",
       "      <td>5.1192</td>\n",
       "      <td>1099.9</td>\n",
       "      <td>524.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20829</th>\n",
       "      <td>17.6730</td>\n",
       "      <td>1020.7</td>\n",
       "      <td>88.840</td>\n",
       "      <td>3.0370</td>\n",
       "      <td>1079.9</td>\n",
       "      <td>550.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29386 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AT      AP      AH    AFDP     TIT     TAT\n",
       "4481   26.6350  1009.7  83.256  4.4137  1100.0  540.65\n",
       "24884  20.2280  1016.2  73.583  4.6238  1099.8  538.53\n",
       "21558  15.6330  1018.5  81.089  4.0899  1100.0  534.04\n",
       "1706   16.6540  1020.2  64.757  4.5755  1086.6  549.76\n",
       "21389  21.0020  1004.3  75.645  4.1101  1100.0  534.21\n",
       "...        ...     ...     ...     ...     ...     ...\n",
       "25726  17.5190  1015.9  85.663  3.6809  1072.2  549.82\n",
       "5015   21.9780  1014.4  75.280  3.1246  1058.0  549.86\n",
       "22585   4.7103  1003.0  92.874  3.2741  1067.2  550.15\n",
       "502     6.7758  1008.3  93.029  5.1192  1099.9  524.78\n",
       "20829  17.6730  1020.7  88.840  3.0370  1079.9  550.02\n",
       "\n",
       "[29386 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4481     0.3527\n",
       "24884    1.2522\n",
       "21558    1.4718\n",
       "1706     1.3117\n",
       "21389    1.7835\n",
       "          ...  \n",
       "25726    2.4980\n",
       "5015     3.2652\n",
       "22585    1.2630\n",
       "502      0.7851\n",
       "20829    2.7272\n",
       "Name: CO, Length: 29386, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>AT</th>\n",
       "      <th>AP</th>\n",
       "      <th>AH</th>\n",
       "      <th>AFDP</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18247</th>\n",
       "      <td>23.4530</td>\n",
       "      <td>1006.2</td>\n",
       "      <td>84.837</td>\n",
       "      <td>3.7535</td>\n",
       "      <td>1088.7</td>\n",
       "      <td>550.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20344</th>\n",
       "      <td>28.7090</td>\n",
       "      <td>1011.2</td>\n",
       "      <td>59.574</td>\n",
       "      <td>6.0321</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>542.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>21.8330</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>81.262</td>\n",
       "      <td>3.9663</td>\n",
       "      <td>1092.9</td>\n",
       "      <td>544.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.8167</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>88.135</td>\n",
       "      <td>4.6605</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>526.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5714</th>\n",
       "      <td>19.9120</td>\n",
       "      <td>1013.1</td>\n",
       "      <td>86.846</td>\n",
       "      <td>3.6710</td>\n",
       "      <td>1080.2</td>\n",
       "      <td>550.25</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21918</th>\n",
       "      <td>9.5791</td>\n",
       "      <td>1017.5</td>\n",
       "      <td>75.935</td>\n",
       "      <td>2.9617</td>\n",
       "      <td>1081.1</td>\n",
       "      <td>549.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13100</th>\n",
       "      <td>22.6150</td>\n",
       "      <td>1012.1</td>\n",
       "      <td>78.314</td>\n",
       "      <td>4.2739</td>\n",
       "      <td>1089.8</td>\n",
       "      <td>550.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26705</th>\n",
       "      <td>28.4020</td>\n",
       "      <td>1004.4</td>\n",
       "      <td>79.478</td>\n",
       "      <td>4.0643</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>550.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4183</th>\n",
       "      <td>31.7400</td>\n",
       "      <td>1012.2</td>\n",
       "      <td>41.623</td>\n",
       "      <td>4.5323</td>\n",
       "      <td>1100.2</td>\n",
       "      <td>539.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>23.7130</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>69.233</td>\n",
       "      <td>3.7112</td>\n",
       "      <td>1091.6</td>\n",
       "      <td>549.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7347 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AT      AP      AH    AFDP     TIT     TAT\n",
       "18247  23.4530  1006.2  84.837  3.7535  1088.7  550.39\n",
       "20344  28.7090  1011.2  59.574  6.0321  1100.0  542.01\n",
       "2925   21.8330  1017.0  81.262  3.9663  1092.9  544.91\n",
       "118     7.8167  1022.2  88.135  4.6605  1100.0  526.21\n",
       "5714   19.9120  1013.1  86.846  3.6710  1080.2  550.25\n",
       "...        ...     ...     ...     ...     ...     ...\n",
       "21918   9.5791  1017.5  75.935  2.9617  1081.1  549.66\n",
       "13100  22.6150  1012.1  78.314  4.2739  1089.8  550.37\n",
       "26705  28.4020  1004.4  79.478  4.0643  1073.0  550.19\n",
       "4183   31.7400  1012.2  41.623  4.5323  1100.2  539.10\n",
       "2983   23.7130  1013.5  69.233  3.7112  1091.6  549.98\n",
       "\n",
       "[7347 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "18247    1.34970\n",
       "20344    1.63430\n",
       "2925     0.78632\n",
       "118      0.72742\n",
       "5714     1.35980\n",
       "          ...   \n",
       "21918    1.45140\n",
       "13100    1.00960\n",
       "26705    2.01190\n",
       "4183     0.37685\n",
       "2983     1.15990\n",
       "Name: CO, Length: 7347, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "y = data[\"CO\"]\n",
    "X = data.drop([\"CO\"], axis=1).copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=random_state\n",
    ")\n",
    "display(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model, tree, neighbors, ensemble\n",
    "\n",
    "models = {\n",
    "    \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
    "    \"linear_poly\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(degree=2),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"linear_interact\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(interaction_only=True),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeRegressor(max_depth=4, random_state=random_state)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestRegressor(\n",
    "            max_depth=7, random_state=random_state, n_jobs=-1\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: linear\n",
      "Model: linear_poly\n",
      "Model: linear_interact\n",
      "Model: ridge\n",
      "Model: decision_tree\n",
      "Model: knn\n",
      "Model: random_forest\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "for model_name in models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    fitted_model = models[model_name][\"model\"].fit(\n",
    "        X_train.values, y_train.values.ravel()\n",
    "    )\n",
    "    y_train_pred = fitted_model.predict(X_train.values)\n",
    "    y_test_pred = fitted_model.predict(X_test.values)\n",
    "    models[model_name][\"fitted\"] = fitted_model\n",
    "    models[model_name][\"train_preds\"] = y_train_pred\n",
    "    models[model_name][\"preds\"] = y_test_pred\n",
    "    models[model_name][\"RMSE_train\"] = math.sqrt(\n",
    "        metrics.mean_squared_error(y_train, y_train_pred)\n",
    "    )\n",
    "    models[model_name][\"RMSE_test\"] = math.sqrt(\n",
    "        metrics.mean_squared_error(y_test, y_test_pred)\n",
    "    )\n",
    "    models[model_name][\"RMAE_test\"] = math.sqrt(\n",
    "        metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    )\n",
    "    models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_1a708\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1a708_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
       "      <th id=\"T_1a708_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
       "      <th id=\"T_1a708_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
       "      <th id=\"T_1a708_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row0\" class=\"row_heading level0 row0\" >random_forest</th>\n",
       "      <td id=\"T_1a708_row0_col0\" class=\"data row0 col0\" >1.023025</td>\n",
       "      <td id=\"T_1a708_row0_col1\" class=\"data row0 col1\" >1.129226</td>\n",
       "      <td id=\"T_1a708_row0_col2\" class=\"data row0 col2\" >0.782178</td>\n",
       "      <td id=\"T_1a708_row0_col3\" class=\"data row0 col3\" >0.748271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
       "      <td id=\"T_1a708_row1_col0\" class=\"data row1 col0\" >1.098849</td>\n",
       "      <td id=\"T_1a708_row1_col1\" class=\"data row1 col1\" >1.203358</td>\n",
       "      <td id=\"T_1a708_row1_col2\" class=\"data row1 col2\" >0.787677</td>\n",
       "      <td id=\"T_1a708_row1_col3\" class=\"data row1 col3\" >0.714135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row2\" class=\"row_heading level0 row2\" >linear_poly</th>\n",
       "      <td id=\"T_1a708_row2_col0\" class=\"data row2 col0\" >1.305816</td>\n",
       "      <td id=\"T_1a708_row2_col1\" class=\"data row2 col1\" >1.261614</td>\n",
       "      <td id=\"T_1a708_row2_col2\" class=\"data row2 col2\" >0.829701</td>\n",
       "      <td id=\"T_1a708_row2_col3\" class=\"data row2 col3\" >0.685788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row3\" class=\"row_heading level0 row3\" >linear_interact</th>\n",
       "      <td id=\"T_1a708_row3_col0\" class=\"data row3 col0\" >1.314473</td>\n",
       "      <td id=\"T_1a708_row3_col1\" class=\"data row3 col1\" >1.272674</td>\n",
       "      <td id=\"T_1a708_row3_col2\" class=\"data row3 col2\" >0.838792</td>\n",
       "      <td id=\"T_1a708_row3_col3\" class=\"data row3 col3\" >0.680254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row4\" class=\"row_heading level0 row4\" >decision_tree</th>\n",
       "      <td id=\"T_1a708_row4_col0\" class=\"data row4 col0\" >1.286532</td>\n",
       "      <td id=\"T_1a708_row4_col1\" class=\"data row4 col1\" >1.275301</td>\n",
       "      <td id=\"T_1a708_row4_col2\" class=\"data row4 col2\" >0.845881</td>\n",
       "      <td id=\"T_1a708_row4_col3\" class=\"data row4 col3\" >0.678932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row5\" class=\"row_heading level0 row5\" >linear</th>\n",
       "      <td id=\"T_1a708_row5_col0\" class=\"data row5 col0\" >1.512818</td>\n",
       "      <td id=\"T_1a708_row5_col1\" class=\"data row5 col1\" >1.484769</td>\n",
       "      <td id=\"T_1a708_row5_col2\" class=\"data row5 col2\" >0.935421</td>\n",
       "      <td id=\"T_1a708_row5_col3\" class=\"data row5 col3\" >0.564800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a708_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
       "      <td id=\"T_1a708_row6_col0\" class=\"data row6 col0\" >1.512818</td>\n",
       "      <td id=\"T_1a708_row6_col1\" class=\"data row6 col1\" >1.484770</td>\n",
       "      <td id=\"T_1a708_row6_col2\" class=\"data row6 col2\" >0.935421</td>\n",
       "      <td id=\"T_1a708_row6_col3\" class=\"data row6 col3\" >0.564800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1249f7500>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n",
    "    [\"RMSE_train\", \"RMSE_test\", \"RMAE_test\", \"R2_test\"]\n",
    "]\n",
    "reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n",
    "    cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n",
    ").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"RMAE_test\", \"R2_test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/numpy/ma/core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'squared_error', 'max_depth': 9, 'min_samples_split': 10}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "\n",
    "parameters = {\n",
    "    \"criterion\": [\"squared_error\", \"absolute_error\", \"friedman_mse\", \"poisson\"],\n",
    "    \"max_depth\": np.arange(1, 21).tolist()[0::2],\n",
    "    \"min_samples_split\": np.arange(2, 11).tolist()[0::2],\n",
    "}\n",
    "\n",
    "grid = model_selection.GridSearchCV(\n",
    "    tree.DecisionTreeRegressor(random_state=random_state), parameters, cv=5, n_jobs=-1, scoring=\"r2\"\n",
    ")\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 1.2753013703532543,\n",
       " 'RMAE_test': 0.8458813419529052,\n",
       " 'R2_test': 0.6789324577873092}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 1.2544701838600494,\n",
       " 'RMAE_test': 0.7821057715152305,\n",
       " 'R2_test': 0.6893356365847731}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = grid.best_estimator_\n",
    "y_pred = model.predict(X_test)\n",
    "old_metrics = {\n",
    "    \"RMSE_test\": models[\"decision_tree\"][\"RMSE_test\"],\n",
    "    \"RMAE_test\": models[\"decision_tree\"][\"RMAE_test\"],\n",
    "    \"R2_test\": models[\"decision_tree\"][\"R2_test\"],\n",
    "}\n",
    "new_metrics = {}\n",
    "new_metrics[\"RMSE_test\"] = math.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
    "new_metrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(y_test, y_pred)\n",
    ")\n",
    "new_metrics[\"R2_test\"] = metrics.r2_score(y_test, y_pred)\n",
    "\n",
    "display(old_metrics)\n",
    "display(new_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- TIT <= 1058.15\n",
      "|   |--- TAT <= 543.87\n",
      "|   |   |--- TAT <= 528.12\n",
      "|   |   |   |--- AP <= 1023.00\n",
      "|   |   |   |   |--- AT <= 4.38\n",
      "|   |   |   |   |   |--- value: [11.50]\n",
      "|   |   |   |   |--- AT >  4.38\n",
      "|   |   |   |   |   |--- AH <= 86.80\n",
      "|   |   |   |   |   |   |--- AP <= 1017.20\n",
      "|   |   |   |   |   |   |   |--- value: [34.96]\n",
      "|   |   |   |   |   |   |--- AP >  1017.20\n",
      "|   |   |   |   |   |   |   |--- value: [27.31]\n",
      "|   |   |   |   |   |--- AH >  86.80\n",
      "|   |   |   |   |   |   |--- value: [40.94]\n",
      "|   |   |   |--- AP >  1023.00\n",
      "|   |   |   |   |--- value: [11.34]\n",
      "|   |   |--- TAT >  528.12\n",
      "|   |   |   |--- TIT <= 1028.85\n",
      "|   |   |   |   |--- AT <= 15.97\n",
      "|   |   |   |   |   |--- TIT <= 1018.85\n",
      "|   |   |   |   |   |   |--- value: [20.73]\n",
      "|   |   |   |   |   |--- TIT >  1018.85\n",
      "|   |   |   |   |   |   |--- AP <= 1020.75\n",
      "|   |   |   |   |   |   |   |--- AT <= 14.71\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 95.05\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [11.90]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  95.05\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.33]\n",
      "|   |   |   |   |   |   |   |--- AT >  14.71\n",
      "|   |   |   |   |   |   |   |   |--- value: [16.37]\n",
      "|   |   |   |   |   |   |--- AP >  1020.75\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.49\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 7.53\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [16.59]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  7.53\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.82]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.49\n",
      "|   |   |   |   |   |   |   |   |--- value: [27.33]\n",
      "|   |   |   |   |--- AT >  15.97\n",
      "|   |   |   |   |   |--- value: [21.87]\n",
      "|   |   |   |--- TIT >  1028.85\n",
      "|   |   |   |   |--- AT <= 24.49\n",
      "|   |   |   |   |   |--- AP <= 997.04\n",
      "|   |   |   |   |   |   |--- value: [27.12]\n",
      "|   |   |   |   |   |--- AP >  997.04\n",
      "|   |   |   |   |   |   |--- TAT <= 537.71\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1046.95\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.01\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [12.56]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.01\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.29]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1046.95\n",
      "|   |   |   |   |   |   |   |   |--- value: [19.61]\n",
      "|   |   |   |   |   |   |--- TAT >  537.71\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.17\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 542.42\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.93]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  542.42\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.71]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.17\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1041.75\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.76]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1041.75\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.32]\n",
      "|   |   |   |   |--- AT >  24.49\n",
      "|   |   |   |   |   |--- value: [35.40]\n",
      "|   |--- TAT >  543.87\n",
      "|   |   |--- TAT <= 549.23\n",
      "|   |   |   |--- TIT <= 1049.65\n",
      "|   |   |   |   |--- TAT <= 548.03\n",
      "|   |   |   |   |   |--- TIT <= 1034.35\n",
      "|   |   |   |   |   |   |--- AFDP <= 2.78\n",
      "|   |   |   |   |   |   |   |--- AH <= 89.28\n",
      "|   |   |   |   |   |   |   |   |--- value: [9.67]\n",
      "|   |   |   |   |   |   |   |--- AH >  89.28\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.48]\n",
      "|   |   |   |   |   |   |--- AFDP >  2.78\n",
      "|   |   |   |   |   |   |   |--- value: [11.30]\n",
      "|   |   |   |   |   |--- TIT >  1034.35\n",
      "|   |   |   |   |   |   |--- AFDP <= 2.69\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 2.44\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1022.90\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.91]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1022.90\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.45]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  2.44\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 546.68\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.42]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  546.68\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.34]\n",
      "|   |   |   |   |   |   |--- AFDP >  2.69\n",
      "|   |   |   |   |   |   |   |--- AT <= 11.97\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1016.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.98]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1016.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.32]\n",
      "|   |   |   |   |   |   |   |--- AT >  11.97\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 78.34\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.37]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  78.34\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.73]\n",
      "|   |   |   |   |--- TAT >  548.03\n",
      "|   |   |   |   |   |--- AP <= 1024.55\n",
      "|   |   |   |   |   |   |--- AH <= 99.53\n",
      "|   |   |   |   |   |   |   |--- AH <= 94.23\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 2.69\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.04]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  2.69\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.51]\n",
      "|   |   |   |   |   |   |   |--- AH >  94.23\n",
      "|   |   |   |   |   |   |   |   |--- value: [7.39]\n",
      "|   |   |   |   |   |   |--- AH >  99.53\n",
      "|   |   |   |   |   |   |   |--- value: [3.34]\n",
      "|   |   |   |   |   |--- AP >  1024.55\n",
      "|   |   |   |   |   |   |--- TIT <= 1046.95\n",
      "|   |   |   |   |   |   |   |--- AH <= 74.83\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.56]\n",
      "|   |   |   |   |   |   |   |--- AH >  74.83\n",
      "|   |   |   |   |   |   |   |   |--- value: [8.46]\n",
      "|   |   |   |   |   |   |--- TIT >  1046.95\n",
      "|   |   |   |   |   |   |   |--- value: [4.51]\n",
      "|   |   |   |--- TIT >  1049.65\n",
      "|   |   |   |   |--- AFDP <= 2.83\n",
      "|   |   |   |   |   |--- AP <= 1016.50\n",
      "|   |   |   |   |   |   |--- TIT <= 1051.65\n",
      "|   |   |   |   |   |   |   |--- TAT <= 548.73\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 2.61\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.54]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  2.61\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.82]\n",
      "|   |   |   |   |   |   |   |--- TAT >  548.73\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 998.10\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.18]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  998.10\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.59]\n",
      "|   |   |   |   |   |   |--- TIT >  1051.65\n",
      "|   |   |   |   |   |   |   |--- AP <= 1007.45\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.40]\n",
      "|   |   |   |   |   |   |   |--- AP >  1007.45\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 544.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.99]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  544.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.62]\n",
      "|   |   |   |   |   |--- AP >  1016.50\n",
      "|   |   |   |   |   |   |--- TAT <= 547.32\n",
      "|   |   |   |   |   |   |   |--- AT <= 8.60\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.10]\n",
      "|   |   |   |   |   |   |   |--- AT >  8.60\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 85.23\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [7.70]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  85.23\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.72]\n",
      "|   |   |   |   |   |   |--- TAT >  547.32\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 2.55\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1019.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.14]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1019.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.92]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  2.55\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1021.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.09]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1021.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.61]\n",
      "|   |   |   |   |--- AFDP >  2.83\n",
      "|   |   |   |   |   |--- TAT <= 546.71\n",
      "|   |   |   |   |   |   |--- AT <= 15.83\n",
      "|   |   |   |   |   |   |   |--- TAT <= 544.64\n",
      "|   |   |   |   |   |   |   |   |--- value: [7.07]\n",
      "|   |   |   |   |   |   |   |--- TAT >  544.64\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 546.43\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.87]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  546.43\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.30]\n",
      "|   |   |   |   |   |   |--- AT >  15.83\n",
      "|   |   |   |   |   |   |   |--- value: [9.77]\n",
      "|   |   |   |   |   |--- TAT >  546.71\n",
      "|   |   |   |   |   |   |--- AH <= 82.62\n",
      "|   |   |   |   |   |   |   |--- AP <= 1004.20\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.96]\n",
      "|   |   |   |   |   |   |   |--- AP >  1004.20\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.01\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.96]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.01\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.68]\n",
      "|   |   |   |   |   |   |--- AH >  82.62\n",
      "|   |   |   |   |   |   |   |--- AT <= 12.90\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 97.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.47]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  97.26\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.90]\n",
      "|   |   |   |   |   |   |   |--- AT >  12.90\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1053.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.45]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1053.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.00]\n",
      "|   |   |--- TAT >  549.23\n",
      "|   |   |   |--- AFDP <= 2.71\n",
      "|   |   |   |   |--- AFDP <= 2.41\n",
      "|   |   |   |   |   |--- TIT <= 1050.75\n",
      "|   |   |   |   |   |   |--- AP <= 1015.45\n",
      "|   |   |   |   |   |   |   |--- AT <= 10.60\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.55]\n",
      "|   |   |   |   |   |   |   |--- AT >  10.60\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 550.46\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.90]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  550.46\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.99]\n",
      "|   |   |   |   |   |   |--- AP >  1015.45\n",
      "|   |   |   |   |   |   |   |--- AT <= 13.80\n",
      "|   |   |   |   |   |   |   |   |--- value: [3.58]\n",
      "|   |   |   |   |   |   |   |--- AT >  13.80\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 20.03\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.53]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  20.03\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.39]\n",
      "|   |   |   |   |   |--- TIT >  1050.75\n",
      "|   |   |   |   |   |   |--- TIT <= 1053.65\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 2.18\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.89]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  2.18\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1005.15\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.58]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1005.15\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.15]\n",
      "|   |   |   |   |   |   |--- TIT >  1053.65\n",
      "|   |   |   |   |   |   |   |--- AT <= 15.24\n",
      "|   |   |   |   |   |   |   |   |--- value: [4.75]\n",
      "|   |   |   |   |   |   |   |--- AT >  15.24\n",
      "|   |   |   |   |   |   |   |   |--- value: [3.96]\n",
      "|   |   |   |   |--- AFDP >  2.41\n",
      "|   |   |   |   |   |--- AT <= 14.12\n",
      "|   |   |   |   |   |   |--- TIT <= 1052.25\n",
      "|   |   |   |   |   |   |   |--- AP <= 1021.85\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1044.35\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.14]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1044.35\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.86]\n",
      "|   |   |   |   |   |   |   |--- AP >  1021.85\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 1.44\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.53]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  1.44\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.58]\n",
      "|   |   |   |   |   |   |--- TIT >  1052.25\n",
      "|   |   |   |   |   |   |   |--- TAT <= 550.09\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1018.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.59]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1018.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.67]\n",
      "|   |   |   |   |   |   |   |--- TAT >  550.09\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1025.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.92]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1025.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.60]\n",
      "|   |   |   |   |   |--- AT >  14.12\n",
      "|   |   |   |   |   |   |--- TIT <= 1042.80\n",
      "|   |   |   |   |   |   |   |--- AP <= 1012.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.84]\n",
      "|   |   |   |   |   |   |   |--- AP >  1012.00\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.98]\n",
      "|   |   |   |   |   |   |--- TIT >  1042.80\n",
      "|   |   |   |   |   |   |   |--- AT <= 15.87\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 14.25\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.54]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  14.25\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.16]\n",
      "|   |   |   |   |   |   |   |--- AT >  15.87\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 55.76\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.75]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  55.76\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.72]\n",
      "|   |   |   |--- AFDP >  2.71\n",
      "|   |   |   |   |--- TIT <= 1056.05\n",
      "|   |   |   |   |   |--- AT <= 12.67\n",
      "|   |   |   |   |   |   |--- AH <= 91.10\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 2.86\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 11.95\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.53]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  11.95\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.65]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  2.86\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1045.05\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.65]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1045.05\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.76]\n",
      "|   |   |   |   |   |   |--- AH >  91.10\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.01\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 99.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.88]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  99.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.91]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.01\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1047.40\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.59]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1047.40\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.57]\n",
      "|   |   |   |   |   |--- AT >  12.67\n",
      "|   |   |   |   |   |   |--- AT <= 23.88\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.91\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 88.32\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.53]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  88.32\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.99]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.91\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 5.03\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.93]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  5.03\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.07]\n",
      "|   |   |   |   |   |   |--- AT >  23.88\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1050.35\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1041.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.48]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1041.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.38]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1050.35\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1011.25\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.00]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1011.25\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.61]\n",
      "|   |   |   |   |--- TIT >  1056.05\n",
      "|   |   |   |   |   |--- AFDP <= 3.14\n",
      "|   |   |   |   |   |   |--- AFDP <= 2.87\n",
      "|   |   |   |   |   |   |   |--- AP <= 999.86\n",
      "|   |   |   |   |   |   |   |   |--- value: [5.27]\n",
      "|   |   |   |   |   |   |   |--- AP >  999.86\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1056.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.91]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1056.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.47]\n",
      "|   |   |   |   |   |   |--- AFDP >  2.87\n",
      "|   |   |   |   |   |   |   |--- AH <= 85.90\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1056.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.86]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1056.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.04]\n",
      "|   |   |   |   |   |   |   |--- AH >  85.90\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.02\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.18]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.02\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.49]\n",
      "|   |   |   |   |   |--- AFDP >  3.14\n",
      "|   |   |   |   |   |   |--- AH <= 80.23\n",
      "|   |   |   |   |   |   |   |--- AH <= 59.72\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 25.08\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.43]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  25.08\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.28]\n",
      "|   |   |   |   |   |   |   |--- AH >  59.72\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.16]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.48]\n",
      "|   |   |   |   |   |   |--- AH >  80.23\n",
      "|   |   |   |   |   |   |   |--- AT <= 17.64\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1008.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.34]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1008.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.39]\n",
      "|   |   |   |   |   |   |   |--- AT >  17.64\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.14\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.63]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.14\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.79]\n",
      "|--- TIT >  1058.15\n",
      "|   |--- TIT <= 1076.55\n",
      "|   |   |--- TAT <= 545.34\n",
      "|   |   |   |--- TIT <= 1076.45\n",
      "|   |   |   |   |--- TAT <= 539.96\n",
      "|   |   |   |   |   |--- AP <= 1007.90\n",
      "|   |   |   |   |   |   |--- value: [33.91]\n",
      "|   |   |   |   |   |--- AP >  1007.90\n",
      "|   |   |   |   |   |   |--- TAT <= 539.63\n",
      "|   |   |   |   |   |   |   |--- AT <= 18.28\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1059.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [13.67]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1059.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.79]\n",
      "|   |   |   |   |   |   |   |--- AT >  18.28\n",
      "|   |   |   |   |   |   |   |   |--- value: [21.75]\n",
      "|   |   |   |   |   |   |--- TAT >  539.63\n",
      "|   |   |   |   |   |   |   |--- value: [29.34]\n",
      "|   |   |   |   |--- TAT >  539.96\n",
      "|   |   |   |   |   |--- TIT <= 1069.35\n",
      "|   |   |   |   |   |   |--- AH <= 98.54\n",
      "|   |   |   |   |   |   |   |--- AT <= 4.45\n",
      "|   |   |   |   |   |   |   |   |--- value: [7.90]\n",
      "|   |   |   |   |   |   |   |--- AT >  4.45\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 542.62\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.97]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  542.62\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.26]\n",
      "|   |   |   |   |   |   |--- AH >  98.54\n",
      "|   |   |   |   |   |   |   |--- value: [10.20]\n",
      "|   |   |   |   |   |--- TIT >  1069.35\n",
      "|   |   |   |   |   |   |--- AP <= 1013.40\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1069.70\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.39]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1069.70\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 69.87\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.01]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  69.87\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.22]\n",
      "|   |   |   |   |   |   |--- AP >  1013.40\n",
      "|   |   |   |   |   |   |   |--- value: [2.26]\n",
      "|   |   |   |--- TIT >  1076.45\n",
      "|   |   |   |   |--- value: [30.38]\n",
      "|   |   |--- TAT >  545.34\n",
      "|   |   |   |--- TAT <= 549.52\n",
      "|   |   |   |   |--- TIT <= 1068.55\n",
      "|   |   |   |   |   |--- AFDP <= 3.20\n",
      "|   |   |   |   |   |   |--- TAT <= 549.07\n",
      "|   |   |   |   |   |   |   |--- TAT <= 547.68\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1059.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.87]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1059.50\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.32]\n",
      "|   |   |   |   |   |   |   |--- TAT >  547.68\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 93.33\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.58]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  93.33\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.40]\n",
      "|   |   |   |   |   |   |--- TAT >  549.07\n",
      "|   |   |   |   |   |   |   |--- AT <= 16.12\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 93.78\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.37]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  93.78\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.69]\n",
      "|   |   |   |   |   |   |   |--- AT >  16.12\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.60]\n",
      "|   |   |   |   |   |--- AFDP >  3.20\n",
      "|   |   |   |   |   |   |--- AP <= 1017.70\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1058.25\n",
      "|   |   |   |   |   |   |   |   |--- value: [6.15]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1058.25\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1065.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.42]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1065.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.82]\n",
      "|   |   |   |   |   |   |--- AP >  1017.70\n",
      "|   |   |   |   |   |   |   |--- TAT <= 548.77\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 99.46\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.38]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  99.46\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.01]\n",
      "|   |   |   |   |   |   |   |--- TAT >  548.77\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 96.92\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.39]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  96.92\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.87]\n",
      "|   |   |   |   |--- TIT >  1068.55\n",
      "|   |   |   |   |   |--- AP <= 993.61\n",
      "|   |   |   |   |   |   |--- value: [6.68]\n",
      "|   |   |   |   |   |--- AP >  993.61\n",
      "|   |   |   |   |   |   |--- AT <= 21.17\n",
      "|   |   |   |   |   |   |   |--- AH <= 69.22\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1023.25\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.35]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1023.25\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.32]\n",
      "|   |   |   |   |   |   |   |--- AH >  69.22\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 548.49\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.41]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  548.49\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.91]\n",
      "|   |   |   |   |   |   |--- AT >  21.17\n",
      "|   |   |   |   |   |   |   |--- AP <= 1002.75\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.12]\n",
      "|   |   |   |   |   |   |   |--- AP >  1002.75\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 548.43\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.77]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  548.43\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.06]\n",
      "|   |   |   |--- TAT >  549.52\n",
      "|   |   |   |   |--- TIT <= 1060.85\n",
      "|   |   |   |   |   |--- AFDP <= 3.28\n",
      "|   |   |   |   |   |   |--- AFDP <= 2.97\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 2.58\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.78]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  2.58\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 18.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.28]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  18.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.07]\n",
      "|   |   |   |   |   |   |--- AFDP >  2.97\n",
      "|   |   |   |   |   |   |   |--- AT <= 21.72\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 21.70\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.63]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  21.70\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [8.44]\n",
      "|   |   |   |   |   |   |   |--- AT >  21.72\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 75.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.61]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  75.30\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.04]\n",
      "|   |   |   |   |   |--- AFDP >  3.28\n",
      "|   |   |   |   |   |   |--- AH <= 75.08\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1058.45\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 26.12\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.50]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  26.12\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.50]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1058.45\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1009.95\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.19]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1009.95\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.60]\n",
      "|   |   |   |   |   |   |--- AH >  75.08\n",
      "|   |   |   |   |   |   |   |--- AT <= 19.36\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 550.21\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.64]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  550.21\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.80]\n",
      "|   |   |   |   |   |   |   |--- AT >  19.36\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 97.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.47]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  97.65\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.98]\n",
      "|   |   |   |   |--- TIT >  1060.85\n",
      "|   |   |   |   |   |--- AFDP <= 3.20\n",
      "|   |   |   |   |   |   |--- AFDP <= 2.74\n",
      "|   |   |   |   |   |   |   |--- AT <= 8.46\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 85.16\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.61]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  85.16\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.57]\n",
      "|   |   |   |   |   |   |   |--- AT >  8.46\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1061.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.77]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1061.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.22]\n",
      "|   |   |   |   |   |   |--- AFDP >  2.74\n",
      "|   |   |   |   |   |   |   |--- AT <= 14.98\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 87.67\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.57]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  87.67\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.67]\n",
      "|   |   |   |   |   |   |   |--- AT >  14.98\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 22.93\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.75]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  22.93\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.86]\n",
      "|   |   |   |   |   |--- AFDP >  3.20\n",
      "|   |   |   |   |   |   |--- TIT <= 1069.15\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.35\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1027.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.14]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1027.00\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [4.33]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.35\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 550.46\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.16]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  550.46\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [6.89]\n",
      "|   |   |   |   |   |   |--- TIT >  1069.15\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.36\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 13.18\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.15]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  13.18\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.48]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.36\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 14.27\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.84]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  14.27\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.32]\n",
      "|   |--- TIT >  1076.55\n",
      "|   |   |--- TIT <= 1091.35\n",
      "|   |   |   |--- TAT <= 532.02\n",
      "|   |   |   |   |--- value: [12.04]\n",
      "|   |   |   |--- TAT >  532.02\n",
      "|   |   |   |   |--- AFDP <= 3.49\n",
      "|   |   |   |   |   |--- TIT <= 1085.05\n",
      "|   |   |   |   |   |   |--- AH <= 81.79\n",
      "|   |   |   |   |   |   |   |--- AH <= 81.72\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 16.06\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.69]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  16.06\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.17]\n",
      "|   |   |   |   |   |   |   |--- AH >  81.72\n",
      "|   |   |   |   |   |   |   |   |--- value: [34.47]\n",
      "|   |   |   |   |   |   |--- AH >  81.79\n",
      "|   |   |   |   |   |   |   |--- TAT <= 548.82\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1080.20\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.49]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1080.20\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.17]\n",
      "|   |   |   |   |   |   |   |--- TAT >  548.82\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.33\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.95]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.33\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.54]\n",
      "|   |   |   |   |   |--- TIT >  1085.05\n",
      "|   |   |   |   |   |   |--- AH <= 60.56\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1086.75\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 60.29\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.75]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  60.29\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.23]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1086.75\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1025.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.53]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1025.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.80]\n",
      "|   |   |   |   |   |   |--- AH >  60.56\n",
      "|   |   |   |   |   |   |   |--- TIT <= 1086.55\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.41\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.68]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.41\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.34]\n",
      "|   |   |   |   |   |   |   |--- TIT >  1086.55\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1014.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.62]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1014.55\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.37]\n",
      "|   |   |   |   |--- AFDP >  3.49\n",
      "|   |   |   |   |   |--- AT <= 16.36\n",
      "|   |   |   |   |   |   |--- TAT <= 538.79\n",
      "|   |   |   |   |   |   |   |--- TAT <= 538.74\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 68.06\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [5.82]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  68.06\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.29]\n",
      "|   |   |   |   |   |   |   |--- TAT >  538.74\n",
      "|   |   |   |   |   |   |   |   |--- value: [19.14]\n",
      "|   |   |   |   |   |   |--- TAT >  538.79\n",
      "|   |   |   |   |   |   |   |--- AP <= 1026.95\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 549.53\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.67]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  549.53\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.37]\n",
      "|   |   |   |   |   |   |   |--- AP >  1026.95\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1078.95\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [3.04]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1078.95\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.83]\n",
      "|   |   |   |   |   |--- AT >  16.36\n",
      "|   |   |   |   |   |   |--- TIT <= 1085.55\n",
      "|   |   |   |   |   |   |   |--- AH <= 100.05\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1013.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.82]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1013.45\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.04]\n",
      "|   |   |   |   |   |   |   |--- AH >  100.05\n",
      "|   |   |   |   |   |   |   |   |--- value: [4.52]\n",
      "|   |   |   |   |   |   |--- TIT >  1085.55\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 3.92\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.63\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.62]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.63\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.18]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  3.92\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 4.06\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.43]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  4.06\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.69]\n",
      "|   |   |--- TIT >  1091.35\n",
      "|   |   |   |--- TAT <= 530.62\n",
      "|   |   |   |   |--- AT <= 3.61\n",
      "|   |   |   |   |   |--- AFDP <= 5.31\n",
      "|   |   |   |   |   |   |--- AP <= 1020.15\n",
      "|   |   |   |   |   |   |   |--- AT <= 3.02\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 528.77\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.89]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  528.77\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.91]\n",
      "|   |   |   |   |   |   |   |--- AT >  3.02\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.81]\n",
      "|   |   |   |   |   |   |--- AP >  1020.15\n",
      "|   |   |   |   |   |   |   |--- AH <= 78.41\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 58.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.47]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  58.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.70]\n",
      "|   |   |   |   |   |   |   |--- AH >  78.41\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 530.51\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.44]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  530.51\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.53]\n",
      "|   |   |   |   |   |--- AFDP >  5.31\n",
      "|   |   |   |   |   |   |--- AP <= 1014.10\n",
      "|   |   |   |   |   |   |   |--- value: [0.25]\n",
      "|   |   |   |   |   |   |--- AP >  1014.10\n",
      "|   |   |   |   |   |   |   |--- AT <= 3.11\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 524.86\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.86]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  524.86\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.36]\n",
      "|   |   |   |   |   |   |   |--- AT >  3.11\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.57]\n",
      "|   |   |   |   |--- AT >  3.61\n",
      "|   |   |   |   |   |--- AFDP <= 5.65\n",
      "|   |   |   |   |   |   |--- AFDP <= 4.00\n",
      "|   |   |   |   |   |   |   |--- AT <= 4.30\n",
      "|   |   |   |   |   |   |   |   |--- value: [2.12]\n",
      "|   |   |   |   |   |   |   |--- AT >  4.30\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.91\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.60]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.91\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.45]\n",
      "|   |   |   |   |   |   |--- AFDP >  4.00\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 4.67\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1018.05\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.09]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1018.05\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.36]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  4.67\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 5.35\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.79]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  5.35\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.11]\n",
      "|   |   |   |   |   |--- AFDP >  5.65\n",
      "|   |   |   |   |   |   |--- TAT <= 517.90\n",
      "|   |   |   |   |   |   |   |--- value: [1.51]\n",
      "|   |   |   |   |   |   |--- TAT >  517.90\n",
      "|   |   |   |   |   |   |   |--- AT <= 8.05\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 527.54\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.35]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  527.54\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.53]\n",
      "|   |   |   |   |   |   |   |--- AT >  8.05\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1027.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.58]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1027.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.82]\n",
      "|   |   |   |--- TAT >  530.62\n",
      "|   |   |   |   |--- AP <= 1023.35\n",
      "|   |   |   |   |   |--- AFDP <= 4.39\n",
      "|   |   |   |   |   |   |--- AT <= 11.92\n",
      "|   |   |   |   |   |   |   |--- AH <= 87.51\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.92\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.68]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.92\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.37]\n",
      "|   |   |   |   |   |   |   |--- AH >  87.51\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 3.61\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.34]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  3.61\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.14]\n",
      "|   |   |   |   |   |   |--- AT >  11.92\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 4.09\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 24.52\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.34]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  24.52\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.88]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  4.09\n",
      "|   |   |   |   |   |   |   |   |--- TAT <= 537.27\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.25]\n",
      "|   |   |   |   |   |   |   |   |--- TAT >  537.27\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.50]\n",
      "|   |   |   |   |   |--- AFDP >  4.39\n",
      "|   |   |   |   |   |   |--- TAT <= 542.35\n",
      "|   |   |   |   |   |   |   |--- AP <= 1014.35\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 4.64\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.80]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  4.64\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.12]\n",
      "|   |   |   |   |   |   |   |--- AP >  1014.35\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 72.22\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.39]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  72.22\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.08]\n",
      "|   |   |   |   |   |   |--- TAT >  542.35\n",
      "|   |   |   |   |   |   |   |--- TAT <= 542.35\n",
      "|   |   |   |   |   |   |   |   |--- value: [11.85]\n",
      "|   |   |   |   |   |   |   |--- TAT >  542.35\n",
      "|   |   |   |   |   |   |   |   |--- AH <= 39.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.36]\n",
      "|   |   |   |   |   |   |   |   |--- AH >  39.82\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.39]\n",
      "|   |   |   |   |--- AP >  1023.35\n",
      "|   |   |   |   |   |--- AFDP <= 4.65\n",
      "|   |   |   |   |   |   |--- AT <= 13.85\n",
      "|   |   |   |   |   |   |   |--- AH <= 56.86\n",
      "|   |   |   |   |   |   |   |   |--- TIT <= 1098.10\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [9.57]\n",
      "|   |   |   |   |   |   |   |   |--- TIT >  1098.10\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.84]\n",
      "|   |   |   |   |   |   |   |--- AH >  56.86\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 3.87\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.45]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  3.87\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [2.23]\n",
      "|   |   |   |   |   |   |--- AT >  13.85\n",
      "|   |   |   |   |   |   |   |--- TAT <= 548.59\n",
      "|   |   |   |   |   |   |   |   |--- AFDP <= 4.10\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.83]\n",
      "|   |   |   |   |   |   |   |   |--- AFDP >  4.10\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.13]\n",
      "|   |   |   |   |   |   |   |--- TAT >  548.59\n",
      "|   |   |   |   |   |   |   |   |--- value: [1.92]\n",
      "|   |   |   |   |   |--- AFDP >  4.65\n",
      "|   |   |   |   |   |   |--- AT <= 4.20\n",
      "|   |   |   |   |   |   |   |--- value: [1.76]\n",
      "|   |   |   |   |   |   |--- AT >  4.20\n",
      "|   |   |   |   |   |   |   |--- AFDP <= 5.11\n",
      "|   |   |   |   |   |   |   |   |--- AP <= 1027.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.99]\n",
      "|   |   |   |   |   |   |   |   |--- AP >  1027.85\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.58]\n",
      "|   |   |   |   |   |   |   |--- AFDP >  5.11\n",
      "|   |   |   |   |   |   |   |   |--- AT <= 13.34\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.53]\n",
      "|   |   |   |   |   |   |   |   |--- AT >  13.34\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [1.17]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rules = tree.export_text(model, feature_names=X_train.columns.values.tolist())\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(model, open(\"data-turbine/tree-gs.model.sav\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- TIT <= 1058.15\n",
      "|   |--- TAT <= 543.87\n",
      "|   |   |--- TAT <= 528.12\n",
      "|   |   |   |--- AP <= 1023.00\n",
      "|   |   |   |   |--- value: [27.13]\n",
      "|   |   |   |--- AP >  1023.00\n",
      "|   |   |   |   |--- value: [11.34]\n",
      "|   |   |--- TAT >  528.12\n",
      "|   |   |   |--- TIT <= 1028.85\n",
      "|   |   |   |   |--- value: [13.71]\n",
      "|   |   |   |--- TIT >  1028.85\n",
      "|   |   |   |   |--- value: [9.47]\n",
      "|   |--- TAT >  543.87\n",
      "|   |   |--- TAT <= 549.23\n",
      "|   |   |   |--- TIT <= 1049.65\n",
      "|   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |--- TIT >  1049.65\n",
      "|   |   |   |   |--- value: [5.61]\n",
      "|   |   |--- TAT >  549.23\n",
      "|   |   |   |--- AFDP <= 2.71\n",
      "|   |   |   |   |--- value: [5.19]\n",
      "|   |   |   |--- AFDP >  2.71\n",
      "|   |   |   |   |--- value: [4.34]\n",
      "|--- TIT >  1058.15\n",
      "|   |--- TIT <= 1076.55\n",
      "|   |   |--- TAT <= 545.34\n",
      "|   |   |   |--- TIT <= 1076.45\n",
      "|   |   |   |   |--- value: [6.29]\n",
      "|   |   |   |--- TIT >  1076.45\n",
      "|   |   |   |   |--- value: [30.38]\n",
      "|   |   |--- TAT >  545.34\n",
      "|   |   |   |--- TAT <= 549.52\n",
      "|   |   |   |   |--- value: [3.87]\n",
      "|   |   |   |--- TAT >  549.52\n",
      "|   |   |   |   |--- value: [2.88]\n",
      "|   |--- TIT >  1076.55\n",
      "|   |   |--- TIT <= 1091.35\n",
      "|   |   |   |--- TAT <= 532.02\n",
      "|   |   |   |   |--- value: [12.04]\n",
      "|   |   |   |--- TAT >  532.02\n",
      "|   |   |   |   |--- value: [1.70]\n",
      "|   |   |--- TIT >  1091.35\n",
      "|   |   |   |--- TAT <= 530.62\n",
      "|   |   |   |   |--- value: [0.96]\n",
      "|   |   |   |--- TAT >  530.62\n",
      "|   |   |   |   |--- value: [1.35]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rules2 = tree.export_text(\n",
    "    models[\"decision_tree\"][\"fitted\"], feature_names=X_train.columns.values.tolist()\n",
    ")\n",
    "print(rules2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(model, open(\"data-turbine/tree.model.sav\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"data-turbine/clear-data.csv\", index=False)"
   ]
  }
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