fuzzy-rules-generator/turbine_regression_co.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
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       "<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>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": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data.hist(bins=30, figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "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>AH</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>83.675</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>84.235</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>84.858</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>85.434</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>85.182</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36729</th>\n",
       "      <td>93.200</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>94.036</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>95.219</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>94.200</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>94.547</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 × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT        CO\n",
       "1      83.675  1086.2  549.83   0.32663\n",
       "2      84.235  1086.1  550.05   0.44784\n",
       "3      84.858  1086.5  550.19   0.45144\n",
       "4      85.434  1086.5  550.17   0.23107\n",
       "5      85.182  1085.9  550.00   0.26747\n",
       "...       ...     ...     ...       ...\n",
       "36729  93.200  1037.0  541.59  10.99300\n",
       "36730  94.036  1037.6  542.28  11.14400\n",
       "36731  95.219  1038.0  543.48  11.41400\n",
       "36732  94.200  1076.9  550.11   3.31340\n",
       "36733  94.547  1067.9  548.23  11.98100\n",
       "\n",
       "[36733 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\"AT\", \"AP\", \"AFDP\", \"GTEP\", \"TEY\", \"CDP\", \"NOX\"], axis=1, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AH</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4481</th>\n",
       "      <td>83.256</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>540.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24884</th>\n",
       "      <td>73.583</td>\n",
       "      <td>1099.8</td>\n",
       "      <td>538.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21558</th>\n",
       "      <td>81.089</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>64.757</td>\n",
       "      <td>1086.6</td>\n",
       "      <td>549.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21389</th>\n",
       "      <td>75.645</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25726</th>\n",
       "      <td>85.663</td>\n",
       "      <td>1072.2</td>\n",
       "      <td>549.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>75.280</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>549.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22585</th>\n",
       "      <td>92.874</td>\n",
       "      <td>1067.2</td>\n",
       "      <td>550.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>93.029</td>\n",
       "      <td>1099.9</td>\n",
       "      <td>524.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20829</th>\n",
       "      <td>88.840</td>\n",
       "      <td>1079.9</td>\n",
       "      <td>550.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29386 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT\n",
       "4481   83.256  1100.0  540.65\n",
       "24884  73.583  1099.8  538.53\n",
       "21558  81.089  1100.0  534.04\n",
       "1706   64.757  1086.6  549.76\n",
       "21389  75.645  1100.0  534.21\n",
       "...       ...     ...     ...\n",
       "25726  85.663  1072.2  549.82\n",
       "5015   75.280  1058.0  549.86\n",
       "22585  92.874  1067.2  550.15\n",
       "502    93.029  1099.9  524.78\n",
       "20829  88.840  1079.9  550.02\n",
       "\n",
       "[29386 rows x 3 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": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>18247</th>\n",
       "      <td>84.837</td>\n",
       "      <td>1088.7</td>\n",
       "      <td>550.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20344</th>\n",
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       "      <td>542.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>81.262</td>\n",
       "      <td>1092.9</td>\n",
       "      <td>544.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>88.135</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>526.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5714</th>\n",
       "      <td>86.846</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21918</th>\n",
       "      <td>75.935</td>\n",
       "      <td>1081.1</td>\n",
       "      <td>549.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13100</th>\n",
       "      <td>78.314</td>\n",
       "      <td>1089.8</td>\n",
       "      <td>550.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26705</th>\n",
       "      <td>79.478</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>550.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4183</th>\n",
       "      <td>41.623</td>\n",
       "      <td>1100.2</td>\n",
       "      <td>539.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2983</th>\n",
       "      <td>69.233</td>\n",
       "      <td>1091.6</td>\n",
       "      <td>549.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7347 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT\n",
       "18247  84.837  1088.7  550.39\n",
       "20344  59.574  1100.0  542.01\n",
       "2925   81.262  1092.9  544.91\n",
       "118    88.135  1100.0  526.21\n",
       "5714   86.846  1080.2  550.25\n",
       "...       ...     ...     ...\n",
       "21918  75.935  1081.1  549.66\n",
       "13100  78.314  1089.8  550.37\n",
       "26705  79.478  1073.0  550.19\n",
       "4183   41.623  1100.2  539.10\n",
       "2983   69.233  1091.6  549.98\n",
       "\n",
       "[7347 rows x 3 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": 37,
   "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": 38,
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "#T_2d500_row4_col2 {\n",
       "  background-color: #9410a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2d500_row4_col3 {\n",
       "  background-color: #ba3388;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2d500_row5_col0, #T_2d500_row5_col1, #T_2d500_row6_col0, #T_2d500_row6_col1 {\n",
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       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2d500\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2d500_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
       "      <th id=\"T_2d500_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
       "      <th id=\"T_2d500_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
       "      <th id=\"T_2d500_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row0\" class=\"row_heading level0 row0\" >random_forest</th>\n",
       "      <td id=\"T_2d500_row0_col0\" class=\"data row0 col0\" >1.098668</td>\n",
       "      <td id=\"T_2d500_row0_col1\" class=\"data row0 col1\" >1.235590</td>\n",
       "      <td id=\"T_2d500_row0_col2\" class=\"data row0 col2\" >0.828348</td>\n",
       "      <td id=\"T_2d500_row0_col3\" class=\"data row0 col3\" >0.698617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
       "      <td id=\"T_2d500_row1_col0\" class=\"data row1 col0\" >1.172648</td>\n",
       "      <td id=\"T_2d500_row1_col1\" class=\"data row1 col1\" >1.277769</td>\n",
       "      <td id=\"T_2d500_row1_col2\" class=\"data row1 col2\" >0.853018</td>\n",
       "      <td id=\"T_2d500_row1_col3\" class=\"data row1 col3\" >0.677689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
       "      <td id=\"T_2d500_row2_col0\" class=\"data row2 col0\" >1.298666</td>\n",
       "      <td id=\"T_2d500_row2_col1\" class=\"data row2 col1\" >1.296005</td>\n",
       "      <td id=\"T_2d500_row2_col2\" class=\"data row2 col2\" >0.850354</td>\n",
       "      <td id=\"T_2d500_row2_col3\" class=\"data row2 col3\" >0.668423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row3\" class=\"row_heading level0 row3\" >linear_poly</th>\n",
       "      <td id=\"T_2d500_row3_col0\" class=\"data row3 col0\" >1.333907</td>\n",
       "      <td id=\"T_2d500_row3_col1\" class=\"data row3 col1\" >1.298067</td>\n",
       "      <td id=\"T_2d500_row3_col2\" class=\"data row3 col2\" >0.848072</td>\n",
       "      <td id=\"T_2d500_row3_col3\" class=\"data row3 col3\" >0.667367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row4\" class=\"row_heading level0 row4\" >linear_interact</th>\n",
       "      <td id=\"T_2d500_row4_col0\" class=\"data row4 col0\" >1.350902</td>\n",
       "      <td id=\"T_2d500_row4_col1\" class=\"data row4 col1\" >1.317669</td>\n",
       "      <td id=\"T_2d500_row4_col2\" class=\"data row4 col2\" >0.872722</td>\n",
       "      <td id=\"T_2d500_row4_col3\" class=\"data row4 col3\" >0.657245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row5\" class=\"row_heading level0 row5\" >ridge</th>\n",
       "      <td id=\"T_2d500_row5_col0\" class=\"data row5 col0\" >1.514847</td>\n",
       "      <td id=\"T_2d500_row5_col1\" class=\"data row5 col1\" >1.485428</td>\n",
       "      <td id=\"T_2d500_row5_col2\" class=\"data row5 col2\" >0.935431</td>\n",
       "      <td id=\"T_2d500_row5_col3\" class=\"data row5 col3\" >0.564414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2d500_level0_row6\" class=\"row_heading level0 row6\" >linear</th>\n",
       "      <td id=\"T_2d500_row6_col0\" class=\"data row6 col0\" >1.514847</td>\n",
       "      <td id=\"T_2d500_row6_col1\" class=\"data row6 col1\" >1.485428</td>\n",
       "      <td id=\"T_2d500_row6_col2\" class=\"data row6 col2\" >0.935432</td>\n",
       "      <td id=\"T_2d500_row6_col3\" class=\"data row6 col3\" >0.564414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x137cabd40>"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'squared_error', 'max_depth': 3, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 40,
     "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 1.2960052722652513,\n",
       " 'RMAE_test': 0.8503538955212363,\n",
       " 'R2_test': 0.6684230843594469}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 1.3135330804507617,\n",
       " 'RMAE_test': 0.859409835559001,\n",
       " 'MAE_test': 0.7385852654555491,\n",
       " 'R2_test': 0.6593936187792568}"
      ]
     },
     "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[\"MAE_test\"] = float(\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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AH</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "      <th>Real</th>\n",
       "      <th>Inferred</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>87.114</td>\n",
       "      <td>1032.5</td>\n",
       "      <td>524.71</td>\n",
       "      <td>43.397</td>\n",
       "      <td>22.393960</td>\n",
       "      <td>21.003040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23462</th>\n",
       "      <td>86.171</td>\n",
       "      <td>1011.7</td>\n",
       "      <td>523.67</td>\n",
       "      <td>34.267</td>\n",
       "      <td>22.393960</td>\n",
       "      <td>11.873040</td>\n",
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       "    <tr>\n",
       "      <th>7567</th>\n",
       "      <td>96.843</td>\n",
       "      <td>1048.1</td>\n",
       "      <td>532.44</td>\n",
       "      <td>31.538</td>\n",
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       "      <td>21.417027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6452</th>\n",
       "      <td>75.234</td>\n",
       "      <td>1086.5</td>\n",
       "      <td>549.41</td>\n",
       "      <td>30.866</td>\n",
       "      <td>1.707870</td>\n",
       "      <td>29.158130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30713</th>\n",
       "      <td>41.576</td>\n",
       "      <td>1085.4</td>\n",
       "      <td>549.99</td>\n",
       "      <td>26.286</td>\n",
       "      <td>1.707870</td>\n",
       "      <td>24.578130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23300</th>\n",
       "      <td>86.811</td>\n",
       "      <td>1020.8</td>\n",
       "      <td>527.23</td>\n",
       "      <td>25.431</td>\n",
       "      <td>22.393960</td>\n",
       "      <td>3.037040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24430</th>\n",
       "      <td>90.582</td>\n",
       "      <td>1032.7</td>\n",
       "      <td>527.79</td>\n",
       "      <td>25.248</td>\n",
       "      <td>22.393960</td>\n",
       "      <td>2.854040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30712</th>\n",
       "      <td>42.412</td>\n",
       "      <td>1085.4</td>\n",
       "      <td>549.83</td>\n",
       "      <td>24.239</td>\n",
       "      <td>1.707870</td>\n",
       "      <td>22.531130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28103</th>\n",
       "      <td>82.969</td>\n",
       "      <td>1021.3</td>\n",
       "      <td>528.98</td>\n",
       "      <td>22.648</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>12.527027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13946</th>\n",
       "      <td>97.060</td>\n",
       "      <td>1036.7</td>\n",
       "      <td>531.58</td>\n",
       "      <td>22.364</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>12.243027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28104</th>\n",
       "      <td>82.795</td>\n",
       "      <td>1023.8</td>\n",
       "      <td>530.13</td>\n",
       "      <td>21.538</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>11.417027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13607</th>\n",
       "      <td>93.559</td>\n",
       "      <td>1059.2</td>\n",
       "      <td>538.95</td>\n",
       "      <td>19.798</td>\n",
       "      <td>6.608783</td>\n",
       "      <td>13.189217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7476</th>\n",
       "      <td>90.801</td>\n",
       "      <td>1062.8</td>\n",
       "      <td>538.95</td>\n",
       "      <td>17.437</td>\n",
       "      <td>6.608783</td>\n",
       "      <td>10.828217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21910</th>\n",
       "      <td>94.108</td>\n",
       "      <td>1024.9</td>\n",
       "      <td>530.78</td>\n",
       "      <td>16.883</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>6.762027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23320</th>\n",
       "      <td>99.787</td>\n",
       "      <td>1036.5</td>\n",
       "      <td>540.31</td>\n",
       "      <td>16.707</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>6.586027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14133</th>\n",
       "      <td>86.278</td>\n",
       "      <td>1056.5</td>\n",
       "      <td>539.51</td>\n",
       "      <td>16.487</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>6.366027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31059</th>\n",
       "      <td>76.429</td>\n",
       "      <td>1020.6</td>\n",
       "      <td>534.28</td>\n",
       "      <td>15.436</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>5.315027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35693</th>\n",
       "      <td>77.444</td>\n",
       "      <td>1024.2</td>\n",
       "      <td>537.25</td>\n",
       "      <td>14.354</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>4.233027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35768</th>\n",
       "      <td>82.608</td>\n",
       "      <td>1023.9</td>\n",
       "      <td>537.28</td>\n",
       "      <td>14.335</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>4.214027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16284</th>\n",
       "      <td>97.666</td>\n",
       "      <td>1037.7</td>\n",
       "      <td>536.87</td>\n",
       "      <td>14.330</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>4.209027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35539</th>\n",
       "      <td>57.388</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>537.06</td>\n",
       "      <td>14.319</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>4.198027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29464</th>\n",
       "      <td>62.448</td>\n",
       "      <td>1023.4</td>\n",
       "      <td>532.80</td>\n",
       "      <td>14.022</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.901027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16143</th>\n",
       "      <td>86.553</td>\n",
       "      <td>1031.2</td>\n",
       "      <td>531.64</td>\n",
       "      <td>13.972</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.851027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36333</th>\n",
       "      <td>85.795</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>533.72</td>\n",
       "      <td>13.921</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.800027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31062</th>\n",
       "      <td>74.602</td>\n",
       "      <td>1024.8</td>\n",
       "      <td>537.19</td>\n",
       "      <td>13.808</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.687027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29492</th>\n",
       "      <td>57.403</td>\n",
       "      <td>1024.9</td>\n",
       "      <td>527.72</td>\n",
       "      <td>13.798</td>\n",
       "      <td>22.393960</td>\n",
       "      <td>8.595960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16309</th>\n",
       "      <td>94.312</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>537.41</td>\n",
       "      <td>13.694</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.573027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17016</th>\n",
       "      <td>91.229</td>\n",
       "      <td>1037.3</td>\n",
       "      <td>536.74</td>\n",
       "      <td>13.535</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.414027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16441</th>\n",
       "      <td>90.749</td>\n",
       "      <td>1038.9</td>\n",
       "      <td>538.70</td>\n",
       "      <td>13.427</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.306027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>74.598</td>\n",
       "      <td>1026.4</td>\n",
       "      <td>531.47</td>\n",
       "      <td>13.401</td>\n",
       "      <td>10.120973</td>\n",
       "      <td>3.280027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT    Real   Inferred       RMSE\n",
       "7674   87.114  1032.5  524.71  43.397  22.393960  21.003040\n",
       "23462  86.171  1011.7  523.67  34.267  22.393960  11.873040\n",
       "7567   96.843  1048.1  532.44  31.538  10.120973  21.417027\n",
       "6452   75.234  1086.5  549.41  30.866   1.707870  29.158130\n",
       "30713  41.576  1085.4  549.99  26.286   1.707870  24.578130\n",
       "23300  86.811  1020.8  527.23  25.431  22.393960   3.037040\n",
       "24430  90.582  1032.7  527.79  25.248  22.393960   2.854040\n",
       "30712  42.412  1085.4  549.83  24.239   1.707870  22.531130\n",
       "28103  82.969  1021.3  528.98  22.648  10.120973  12.527027\n",
       "13946  97.060  1036.7  531.58  22.364  10.120973  12.243027\n",
       "28104  82.795  1023.8  530.13  21.538  10.120973  11.417027\n",
       "13607  93.559  1059.2  538.95  19.798   6.608783  13.189217\n",
       "7476   90.801  1062.8  538.95  17.437   6.608783  10.828217\n",
       "21910  94.108  1024.9  530.78  16.883  10.120973   6.762027\n",
       "23320  99.787  1036.5  540.31  16.707  10.120973   6.586027\n",
       "14133  86.278  1056.5  539.51  16.487  10.120973   6.366027\n",
       "31059  76.429  1020.6  534.28  15.436  10.120973   5.315027\n",
       "35693  77.444  1024.2  537.25  14.354  10.120973   4.233027\n",
       "35768  82.608  1023.9  537.28  14.335  10.120973   4.214027\n",
       "16284  97.666  1037.7  536.87  14.330  10.120973   4.209027\n",
       "35539  57.388  1025.0  537.06  14.319  10.120973   4.198027\n",
       "29464  62.448  1023.4  532.80  14.022  10.120973   3.901027\n",
       "16143  86.553  1031.2  531.64  13.972  10.120973   3.851027\n",
       "36333  85.795  1028.2  533.72  13.921  10.120973   3.800027\n",
       "31062  74.602  1024.8  537.19  13.808  10.120973   3.687027\n",
       "29492  57.403  1024.9  527.72  13.798  22.393960   8.595960\n",
       "16309  94.312  1038.0  537.41  13.694  10.120973   3.573027\n",
       "17016  91.229  1037.3  536.74  13.535  10.120973   3.414027\n",
       "16441  90.749  1038.9  538.70  13.427  10.120973   3.306027\n",
       "6201   74.598  1026.4  531.47  13.401  10.120973   3.280027"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rmse(row):\n",
    "    return math.sqrt(metrics.mean_squared_error([row[\"Real\"]], [row[\"Inferred\"]]))\n",
    "\n",
    "\n",
    "res = X_test.copy()\n",
    "res[\"Real\"] = y_test\n",
    "res[\"Inferred\"] = y_pred\n",
    "res[\"RMSE\"] = res.apply(rmse, axis=1)\n",
    "res.sort_values(by=\"Real\", ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- TIT <= 1058.15\n",
      "|   |--- TAT <= 543.87\n",
      "|   |   |--- TAT <= 528.12\n",
      "|   |   |   |--- value: [22.39]\n",
      "|   |   |--- TAT >  528.12\n",
      "|   |   |   |--- value: [10.12]\n",
      "|   |--- TAT >  543.87\n",
      "|   |   |--- TAT <= 549.23\n",
      "|   |   |   |--- value: [6.44]\n",
      "|   |   |--- TAT >  549.23\n",
      "|   |   |   |--- value: [4.53]\n",
      "|--- TIT >  1058.15\n",
      "|   |--- TIT <= 1076.55\n",
      "|   |   |--- TAT <= 545.34\n",
      "|   |   |   |--- value: [6.61]\n",
      "|   |   |--- TAT >  545.34\n",
      "|   |   |   |--- value: [2.96]\n",
      "|   |--- TIT >  1076.55\n",
      "|   |   |--- TIT <= 1091.35\n",
      "|   |   |   |--- value: [1.71]\n",
      "|   |   |--- TIT >  1091.35\n",
      "|   |   |   |--- value: [1.28]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rules = tree.export_text(model, feature_names=X_train.columns.values.tolist())\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(model, open(\"data-turbine/tree-gs.model.sav\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- TIT <= 1058.15\n",
      "|   |--- TAT <= 543.87\n",
      "|   |   |--- TAT <= 528.12\n",
      "|   |   |   |--- AH <= 92.13\n",
      "|   |   |   |   |--- value: [21.02]\n",
      "|   |   |   |--- AH >  92.13\n",
      "|   |   |   |   |--- value: [41.58]\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",
      "|   |   |   |--- TIT <= 1056.05\n",
      "|   |   |   |   |--- value: [4.70]\n",
      "|   |   |   |--- TIT >  1056.05\n",
      "|   |   |   |   |--- value: [4.08]\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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(\n",
    "    models[\"decision_tree\"][\"fitted\"], open(\"data-turbine/tree.model.sav\", \"wb\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"data-turbine/clear-data.csv\", index=False)"
   ]
  }
 ],
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