{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>0.0</td>\n",
       "      <td>3.707</td>\n",
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       "    <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",
       "  </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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "    }\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>0.0</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>0.0</td>\n",
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       "      <td>2.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Viscosity\n",
       "0  30    0.0   0.0      2.716\n",
       "1  40    0.0   0.0      2.073\n",
       "2  60    0.0   0.0      1.329"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "viscosity_train = pd.read_csv(\"data/viscosity_train.csv\", sep=\";\", decimal=\",\")\n",
    "viscosity_test = pd.read_csv(\"data/viscosity_test.csv\", sep=\";\", decimal=\",\")\n",
    "\n",
    "display(viscosity_train.head(3))\n",
    "display(viscosity_test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</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",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2\n",
       "0  20    0.0   0.0\n",
       "1  25    0.0   0.0\n",
       "2  35    0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    3.707\n",
       "1    3.180\n",
       "2    2.361\n",
       "Name: Viscosity, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2\n",
       "0  30    0.0   0.0\n",
       "1  40    0.0   0.0\n",
       "2  60    0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    2.716\n",
       "1    2.073\n",
       "2    1.329\n",
       "Name: Viscosity, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viscosity_y_train = viscosity_train[\"Viscosity\"]\n",
    "viscosity_train = viscosity_train.drop([\"Viscosity\"], axis=1)\n",
    "\n",
    "display(viscosity_train.head(3))\n",
    "display(viscosity_y_train.head(3))\n",
    "\n",
    "viscosity_y_test = viscosity_test[\"Viscosity\"]\n",
    "viscosity_test = viscosity_test.drop([\"Viscosity\"], axis=1)\n",
    "\n",
    "display(viscosity_test.head(3))\n",
    "display(viscosity_y_test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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(max_depth=7, 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": 4,
   "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",
    "        viscosity_train.values, viscosity_y_train.values.ravel()\n",
    "    )\n",
    "    y_train_pred = fitted_model.predict(viscosity_train.values)\n",
    "    y_test_pred = fitted_model.predict(viscosity_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(viscosity_y_train, y_train_pred)\n",
    "    )\n",
    "    models[model_name][\"RMSE_test\"] = math.sqrt(\n",
    "        metrics.mean_squared_error(viscosity_y_test, y_test_pred)\n",
    "    )\n",
    "    models[model_name][\"RMAE_test\"] = math.sqrt(\n",
    "        metrics.mean_absolute_error(viscosity_y_test, y_test_pred)\n",
    "    )\n",
    "    models[model_name][\"R2_test\"] = metrics.r2_score(viscosity_y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "<table id=\"T_0b35b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0b35b_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
       "      <th id=\"T_0b35b_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
       "      <th id=\"T_0b35b_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
       "      <th id=\"T_0b35b_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row0\" class=\"row_heading level0 row0\" >linear_poly</th>\n",
       "      <td id=\"T_0b35b_row0_col0\" class=\"data row0 col0\" >0.150745</td>\n",
       "      <td id=\"T_0b35b_row0_col1\" class=\"data row0 col1\" >0.139507</td>\n",
       "      <td id=\"T_0b35b_row0_col2\" class=\"data row0 col2\" >0.336239</td>\n",
       "      <td id=\"T_0b35b_row0_col3\" class=\"data row0 col3\" >0.978119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
       "      <td id=\"T_0b35b_row1_col0\" class=\"data row1 col0\" >0.361309</td>\n",
       "      <td id=\"T_0b35b_row1_col1\" class=\"data row1 col1\" >0.303389</td>\n",
       "      <td id=\"T_0b35b_row1_col2\" class=\"data row1 col2\" >0.527911</td>\n",
       "      <td id=\"T_0b35b_row1_col3\" class=\"data row1 col3\" >0.896517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row2\" class=\"row_heading level0 row2\" >random_forest</th>\n",
       "      <td id=\"T_0b35b_row2_col0\" class=\"data row2 col0\" >0.226420</td>\n",
       "      <td id=\"T_0b35b_row2_col1\" class=\"data row2 col1\" >0.341014</td>\n",
       "      <td id=\"T_0b35b_row2_col2\" class=\"data row2 col2\" >0.545765</td>\n",
       "      <td id=\"T_0b35b_row2_col3\" class=\"data row2 col3\" >0.869259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
       "      <td id=\"T_0b35b_row3_col0\" class=\"data row3 col0\" >0.472399</td>\n",
       "      <td id=\"T_0b35b_row3_col1\" class=\"data row3 col1\" >0.378573</td>\n",
       "      <td id=\"T_0b35b_row3_col2\" class=\"data row3 col2\" >0.559409</td>\n",
       "      <td id=\"T_0b35b_row3_col3\" class=\"data row3 col3\" >0.838873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row4\" class=\"row_heading level0 row4\" >decision_tree</th>\n",
       "      <td id=\"T_0b35b_row4_col0\" class=\"data row4 col0\" >0.054533</td>\n",
       "      <td id=\"T_0b35b_row4_col1\" class=\"data row4 col1\" >0.379017</td>\n",
       "      <td id=\"T_0b35b_row4_col2\" class=\"data row4 col2\" >0.587467</td>\n",
       "      <td id=\"T_0b35b_row4_col3\" class=\"data row4 col3\" >0.838495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row5\" class=\"row_heading level0 row5\" >linear</th>\n",
       "      <td id=\"T_0b35b_row5_col0\" class=\"data row5 col0\" >0.441760</td>\n",
       "      <td id=\"T_0b35b_row5_col1\" class=\"data row5 col1\" >0.428940</td>\n",
       "      <td id=\"T_0b35b_row5_col2\" class=\"data row5 col2\" >0.617212</td>\n",
       "      <td id=\"T_0b35b_row5_col3\" class=\"data row5 col3\" >0.793147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0b35b_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_0b35b_row6_col0\" class=\"data row6 col0\" >0.666903</td>\n",
       "      <td id=\"T_0b35b_row6_col1\" class=\"data row6 col1\" >0.566901</td>\n",
       "      <td id=\"T_0b35b_row6_col2\" class=\"data row6 col2\" >0.702700</td>\n",
       "      <td id=\"T_0b35b_row6_col3\" class=\"data row6 col3\" >0.638689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24995879c40>"
      ]
     },
     "execution_count": 5,
     "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": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'poisson', 'max_depth': 9, 'min_samples_split': 2}"
      ]
     },
     "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, 20).tolist()[0::2],\n",
    "}\n",
    "\n",
    "grid = model_selection.GridSearchCV(\n",
    "    tree.DecisionTreeRegressor(random_state=random_state), parameters, n_jobs=-1\n",
    ")\n",
    "\n",
    "grid.fit(viscosity_train, viscosity_y_train)\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 0.37901722760783496,\n",
       " 'RMAE_test': 0.5874671455143883,\n",
       " 'R2_test': 0.8384951109125148}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'RMSE_test': 0.39412315184917696,\n",
       " 'RMAE_test': 0.593196723643326,\n",
       " 'R2_test': 0.8253648477295591}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = grid.best_estimator_\n",
    "y_pred = model.predict(viscosity_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(\n",
    "    metrics.mean_squared_error(viscosity_y_test, y_pred)\n",
    ")\n",
    "new_metrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(viscosity_y_test, y_pred)\n",
    ")\n",
    "new_metrics[\"R2_test\"] = metrics.r2_score(viscosity_y_test, y_pred)\n",
    "\n",
    "display(old_metrics)\n",
    "display(new_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- T <= 32.50\n",
      "|   |--- TiO2 <= 0.18\n",
      "|   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |--- T <= 22.50\n",
      "|   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [3.71]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [4.66]\n",
      "|   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |--- value: [4.88]\n",
      "|   |   |   |--- T >  22.50\n",
      "|   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [3.18]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [3.38]\n",
      "|   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |--- value: [4.24]\n",
      "|   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |--- T <= 22.50\n",
      "|   |   |   |   |--- value: [6.67]\n",
      "|   |   |   |--- T >  22.50\n",
      "|   |   |   |   |--- T <= 27.50\n",
      "|   |   |   |   |   |--- value: [5.59]\n",
      "|   |   |   |   |--- T >  27.50\n",
      "|   |   |   |   |   |--- value: [4.73]\n",
      "|   |--- TiO2 >  0.18\n",
      "|   |   |--- T <= 22.50\n",
      "|   |   |   |--- value: [7.13]\n",
      "|   |   |--- T >  22.50\n",
      "|   |   |   |--- T <= 27.50\n",
      "|   |   |   |   |--- value: [5.87]\n",
      "|   |   |   |--- T >  27.50\n",
      "|   |   |   |   |--- value: [4.94]\n",
      "|--- T >  32.50\n",
      "|   |--- T <= 47.50\n",
      "|   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |   |--- T <= 42.50\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [2.36]\n",
      "|   |   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [2.68]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- T <= 37.50\n",
      "|   |   |   |   |   |   |   |--- value: [3.12]\n",
      "|   |   |   |   |   |   |--- T >  37.50\n",
      "|   |   |   |   |   |   |   |--- value: [2.65]\n",
      "|   |   |   |   |--- T >  42.50\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [1.83]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [2.40]\n",
      "|   |   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |   |--- T <= 37.50\n",
      "|   |   |   |   |   |--- value: [4.12]\n",
      "|   |   |   |   |--- T >  37.50\n",
      "|   |   |   |   |   |--- value: [3.56]\n",
      "|   |   |--- TiO2 >  0.18\n",
      "|   |   |   |--- T <= 40.00\n",
      "|   |   |   |   |--- value: [4.35]\n",
      "|   |   |   |--- T >  40.00\n",
      "|   |   |   |   |--- value: [3.56]\n",
      "|   |--- T >  47.50\n",
      "|   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |   |--- T <= 52.50\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.63]\n",
      "|   |   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.90]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [2.11]\n",
      "|   |   |   |   |--- T >  52.50\n",
      "|   |   |   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.55]\n",
      "|   |   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.66]\n",
      "|   |   |   |   |   |--- T >  65.00\n",
      "|   |   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.19]\n",
      "|   |   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.29]\n",
      "|   |   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |   |--- T <= 57.50\n",
      "|   |   |   |   |   |   |--- value: [2.43]\n",
      "|   |   |   |   |   |--- T >  57.50\n",
      "|   |   |   |   |   |   |--- value: [2.16]\n",
      "|   |   |   |   |--- T >  65.00\n",
      "|   |   |   |   |   |--- value: [1.73]\n",
      "|   |   |--- TiO2 >  0.18\n",
      "|   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |--- T <= 57.50\n",
      "|   |   |   |   |   |--- value: [2.84]\n",
      "|   |   |   |   |--- T >  57.50\n",
      "|   |   |   |   |   |--- value: [2.54]\n",
      "|   |   |   |--- T >  65.00\n",
      "|   |   |   |   |--- value: [1.91]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rules = tree.export_text(\n",
    "    models[\"decision_tree\"][\"fitted\"],\n",
    "    feature_names=viscosity_train.columns.values.tolist(),\n",
    ")\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(models[\"decision_tree\"][\"fitted\"], open(\"data/vtree.model.sav\", \"wb\"))"
   ]
  }
 ],
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