{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Viscosity\n",
       "0  20    0.0   0.0      3.707\n",
       "1  25    0.0   0.0      3.180\n",
       "2  35    0.0   0.0      2.361\n",
       "3  45    0.0   0.0      1.832\n",
       "4  50    0.0   0.0      1.629"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Viscosity\n",
       "0  30   0.00   0.0      2.716\n",
       "1  40   0.00   0.0      2.073\n",
       "2  60   0.00   0.0      1.329\n",
       "3  65   0.00   0.0      1.211\n",
       "4  25   0.05   0.0      4.120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv(\"data/viscosity_train.csv\", sep=\";\", decimal=\",\")\n",
    "test = pd.read_csv(\"data/viscosity_test.csv\", sep=\";\", decimal=\",\")\n",
    "\n",
    "display(train.head())\n",
    "display(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Al2O3  TiO2  Viscosity\n",
       "0    0.0   0.0      3.707\n",
       "1    0.0   0.0      3.180\n",
       "2    0.0   0.0      2.361\n",
       "3    0.0   0.0      1.832\n",
       "4    0.0   0.0      1.629"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    20\n",
       "1    25\n",
       "2    35\n",
       "3    45\n",
       "4    50\n",
       "Name: T, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Al2O3  TiO2  Viscosity\n",
       "0   0.00   0.0      2.716\n",
       "1   0.00   0.0      2.073\n",
       "2   0.00   0.0      1.329\n",
       "3   0.00   0.0      1.211\n",
       "4   0.05   0.0      4.120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    30\n",
       "1    40\n",
       "2    60\n",
       "3    65\n",
       "4    25\n",
       "Name: T, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train = train[\"T\"]\n",
    "X_train = train.drop([\"T\"], axis=1)\n",
    "\n",
    "display(X_train.head())\n",
    "display(y_train.head())\n",
    "\n",
    "y_test = test[\"T\"]\n",
    "X_test = test.drop([\"T\"], axis=1)\n",
    "\n",
    "display(X_test.head())\n",
    "display(y_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
    "random_state = 9\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(random_state=random_state, max_depth=6, criterion=\"absolute_error\")\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": 11,
   "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][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_pred)\n",
    "    models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_pred)\n",
    "    models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_pred)\n",
    "    models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_pred)\n",
    "    models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_pred)\n",
    "    models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6c8fb_row0_col0 {\n",
       "  background-color: #25838e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row0_col1, #T_6c8fb_row5_col0 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row0_col3, #T_6c8fb_row6_col5 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row0_col5, #T_6c8fb_row6_col3 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row1_col0, #T_6c8fb_row1_col1 {\n",
       "  background-color: #1fa187;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row1_col3 {\n",
       "  background-color: #a31e9a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row1_col5, #T_6c8fb_row3_col3 {\n",
       "  background-color: #b83289;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row2_col0 {\n",
       "  background-color: #25ac82;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row2_col1 {\n",
       "  background-color: #32b67a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row2_col3 {\n",
       "  background-color: #b6308b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row2_col5 {\n",
       "  background-color: #9e199d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row3_col0 {\n",
       "  background-color: #2eb37c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row3_col1 {\n",
       "  background-color: #35b779;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row3_col5 {\n",
       "  background-color: #9c179e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row4_col0 {\n",
       "  background-color: #238a8d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row4_col1, #T_6c8fb_row5_col1 {\n",
       "  background-color: #54c568;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6c8fb_row4_col3 {\n",
       "  background-color: #c5407e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row4_col5, #T_6c8fb_row5_col5 {\n",
       "  background-color: #8405a7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row5_col3 {\n",
       "  background-color: #cd4a76;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6c8fb_row6_col0, #T_6c8fb_row6_col1 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6c8fb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6c8fb_level0_col0\" class=\"col_heading level0 col0\" >MSE_train</th>\n",
       "      <th id=\"T_6c8fb_level0_col1\" class=\"col_heading level0 col1\" >MSE_test</th>\n",
       "      <th id=\"T_6c8fb_level0_col2\" class=\"col_heading level0 col2\" >MAE_train</th>\n",
       "      <th id=\"T_6c8fb_level0_col3\" class=\"col_heading level0 col3\" >MAE_test</th>\n",
       "      <th id=\"T_6c8fb_level0_col4\" class=\"col_heading level0 col4\" >R2_train</th>\n",
       "      <th id=\"T_6c8fb_level0_col5\" class=\"col_heading level0 col5\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row0\" class=\"row_heading level0 row0\" >linear_poly</th>\n",
       "      <td id=\"T_6c8fb_row0_col0\" class=\"data row0 col0\" >4.827768</td>\n",
       "      <td id=\"T_6c8fb_row0_col1\" class=\"data row0 col1\" >4.877296</td>\n",
       "      <td id=\"T_6c8fb_row0_col2\" class=\"data row0 col2\" >1.522643</td>\n",
       "      <td id=\"T_6c8fb_row0_col3\" class=\"data row0 col3\" >1.743058</td>\n",
       "      <td id=\"T_6c8fb_row0_col4\" class=\"data row0 col4\" >0.980864</td>\n",
       "      <td id=\"T_6c8fb_row0_col5\" class=\"data row0 col5\" >0.974964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
       "      <td id=\"T_6c8fb_row1_col0\" class=\"data row1 col0\" >21.786348</td>\n",
       "      <td id=\"T_6c8fb_row1_col1\" class=\"data row1 col1\" >23.459572</td>\n",
       "      <td id=\"T_6c8fb_row1_col2\" class=\"data row1 col2\" >3.830996</td>\n",
       "      <td id=\"T_6c8fb_row1_col3\" class=\"data row1 col3\" >4.381115</td>\n",
       "      <td id=\"T_6c8fb_row1_col4\" class=\"data row1 col4\" >0.913644</td>\n",
       "      <td id=\"T_6c8fb_row1_col5\" class=\"data row1 col5\" >0.879577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row2\" class=\"row_heading level0 row2\" >linear</th>\n",
       "      <td id=\"T_6c8fb_row2_col0\" class=\"data row2 col0\" >27.766510</td>\n",
       "      <td id=\"T_6c8fb_row2_col1\" class=\"data row2 col1\" >35.430313</td>\n",
       "      <td id=\"T_6c8fb_row2_col2\" class=\"data row2 col2\" >4.088006</td>\n",
       "      <td id=\"T_6c8fb_row2_col3\" class=\"data row2 col3\" >5.106782</td>\n",
       "      <td id=\"T_6c8fb_row2_col4\" class=\"data row2 col4\" >0.889940</td>\n",
       "      <td id=\"T_6c8fb_row2_col5\" class=\"data row2 col5\" >0.818129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
       "      <td id=\"T_6c8fb_row3_col0\" class=\"data row3 col0\" >31.827476</td>\n",
       "      <td id=\"T_6c8fb_row3_col1\" class=\"data row3 col1\" >36.230606</td>\n",
       "      <td id=\"T_6c8fb_row3_col2\" class=\"data row3 col2\" >4.383008</td>\n",
       "      <td id=\"T_6c8fb_row3_col3\" class=\"data row3 col3\" >5.226480</td>\n",
       "      <td id=\"T_6c8fb_row3_col4\" class=\"data row3 col4\" >0.873843</td>\n",
       "      <td id=\"T_6c8fb_row3_col5\" class=\"data row3 col5\" >0.814021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_6c8fb_row4_col0\" class=\"data row4 col0\" >8.525285</td>\n",
       "      <td id=\"T_6c8fb_row4_col1\" class=\"data row4 col1\" >45.444651</td>\n",
       "      <td id=\"T_6c8fb_row4_col2\" class=\"data row4 col2\" >2.542935</td>\n",
       "      <td id=\"T_6c8fb_row4_col3\" class=\"data row4 col3\" >5.749510</td>\n",
       "      <td id=\"T_6c8fb_row4_col4\" class=\"data row4 col4\" >0.966208</td>\n",
       "      <td id=\"T_6c8fb_row4_col5\" class=\"data row4 col5\" >0.766723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row5\" class=\"row_heading level0 row5\" >decision_tree</th>\n",
       "      <td id=\"T_6c8fb_row5_col0\" class=\"data row5 col0\" >3.289474</td>\n",
       "      <td id=\"T_6c8fb_row5_col1\" class=\"data row5 col1\" >45.588235</td>\n",
       "      <td id=\"T_6c8fb_row5_col2\" class=\"data row5 col2\" >0.921053</td>\n",
       "      <td id=\"T_6c8fb_row5_col3\" class=\"data row5 col3\" >6.176471</td>\n",
       "      <td id=\"T_6c8fb_row5_col4\" class=\"data row5 col4\" >0.986961</td>\n",
       "      <td id=\"T_6c8fb_row5_col5\" class=\"data row5 col5\" >0.765986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6c8fb_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_6c8fb_row6_col0\" class=\"data row6 col0\" >61.855532</td>\n",
       "      <td id=\"T_6c8fb_row6_col1\" class=\"data row6 col1\" >64.165666</td>\n",
       "      <td id=\"T_6c8fb_row6_col2\" class=\"data row6 col2\" >6.522556</td>\n",
       "      <td id=\"T_6c8fb_row6_col3\" class=\"data row6 col3\" >6.806723</td>\n",
       "      <td id=\"T_6c8fb_row6_col4\" class=\"data row6 col4\" >0.754819</td>\n",
       "      <td id=\"T_6c8fb_row6_col5\" class=\"data row6 col5\" >0.670624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11b747260>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n",
    "    [\"MSE_train\", \"MSE_test\", \"MAE_train\", \"MAE_test\", \"R2_train\", \"R2_test\"]\n",
    "]\n",
    "reg_metrics.sort_values(by=\"MAE_test\").style.background_gradient(\n",
    "    cmap=\"viridis\", low=1, high=0.3, subset=[\"MSE_train\", \"MSE_test\"]\n",
    ").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"MAE_test\", \"R2_test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Viscosity <= 2.86\n",
      "|   |--- Viscosity <= 1.38\n",
      "|   |   |--- value: [70.00]\n",
      "|   |--- Viscosity >  1.38\n",
      "|   |   |--- Viscosity <= 1.78\n",
      "|   |   |   |--- Viscosity <= 1.72\n",
      "|   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [52.50]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [57.50]\n",
      "|   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |--- value: [60.00]\n",
      "|   |   |   |--- Viscosity >  1.72\n",
      "|   |   |   |   |--- value: [70.00]\n",
      "|   |   |--- Viscosity >  1.78\n",
      "|   |   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |   |   |--- Viscosity <= 2.24\n",
      "|   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |   |   |   |--- Viscosity >  2.24\n",
      "|   |   |   |   |   |   |--- value: [40.00]\n",
      "|   |   |   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |   |   |--- Viscosity <= 2.29\n",
      "|   |   |   |   |   |   |--- value: [60.00]\n",
      "|   |   |   |   |   |--- Viscosity >  2.29\n",
      "|   |   |   |   |   |   |--- value: [55.00]\n",
      "|   |   |   |--- TiO2 >  0.18\n",
      "|   |   |   |   |--- Viscosity <= 2.22\n",
      "|   |   |   |   |   |--- value: [70.00]\n",
      "|   |   |   |   |--- Viscosity >  2.22\n",
      "|   |   |   |   |   |--- Viscosity <= 2.69\n",
      "|   |   |   |   |   |   |--- value: [60.00]\n",
      "|   |   |   |   |   |--- Viscosity >  2.69\n",
      "|   |   |   |   |   |   |--- value: [55.00]\n",
      "|--- Viscosity >  2.86\n",
      "|   |--- Viscosity <= 3.64\n",
      "|   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |--- Viscosity <= 3.15\n",
      "|   |   |   |   |--- value: [35.00]\n",
      "|   |   |   |--- Viscosity >  3.15\n",
      "|   |   |   |   |--- Viscosity <= 3.47\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [25.00]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [30.00]\n",
      "|   |   |   |   |--- Viscosity >  3.47\n",
      "|   |   |   |   |   |--- value: [40.00]\n",
      "|   |   |--- TiO2 >  0.18\n",
      "|   |   |   |--- value: [45.00]\n",
      "|   |--- Viscosity >  3.64\n",
      "|   |   |--- Viscosity <= 6.27\n",
      "|   |   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [20.00]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [22.50]\n",
      "|   |   |   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |   |   |--- Viscosity <= 4.42\n",
      "|   |   |   |   |   |   |--- value: [35.00]\n",
      "|   |   |   |   |   |--- Viscosity >  4.42\n",
      "|   |   |   |   |   |   |--- value: [27.50]\n",
      "|   |   |   |--- TiO2 >  0.18\n",
      "|   |   |   |   |--- Viscosity <= 4.65\n",
      "|   |   |   |   |   |--- value: [35.00]\n",
      "|   |   |   |   |--- Viscosity >  4.65\n",
      "|   |   |   |   |   |--- Viscosity <= 5.40\n",
      "|   |   |   |   |   |   |--- value: [30.00]\n",
      "|   |   |   |   |   |--- Viscosity >  5.40\n",
      "|   |   |   |   |   |   |--- value: [25.00]\n",
      "|   |   |--- Viscosity >  6.27\n",
      "|   |   |   |--- value: [20.00]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = models[\"decision_tree\"][\"fitted\"]\n",
    "rules = tree.export_text(\n",
    "    model, feature_names=X_train.columns.values.tolist()\n",
    ")\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(model, open(\"data/temp_viscosity_tree.model.sav\", \"wb\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}