{
 "cells": [
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   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
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       "    T  Al2O3  TiO2  Density\n",
       "0  20    0.0   0.0  1.06250\n",
       "1  25    0.0   0.0  1.05979\n",
       "2  35    0.0   0.0  1.05404\n",
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       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Density\n",
       "0  30   0.00   0.0  1.05696\n",
       "1  55   0.00   0.0  1.04158\n",
       "2  25   0.05   0.0  1.08438\n",
       "3  30   0.05   0.0  1.08112\n",
       "4  35   0.05   0.0  1.07781"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv(\"data/density_train.csv\", sep=\";\", decimal=\",\")\n",
    "test = pd.read_csv(\"data/density_test.csv\", sep=\";\", decimal=\",\")\n",
    "\n",
    "display(train.head())\n",
    "display(test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.05979</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.05103</td>\n",
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      ],
      "text/plain": [
       "   Al2O3  TiO2  Density\n",
       "0    0.0   0.0  1.06250\n",
       "1    0.0   0.0  1.05979\n",
       "2    0.0   0.0  1.05404\n",
       "3    0.0   0.0  1.05103\n",
       "4    0.0   0.0  1.04794"
      ]
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       "0    20\n",
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       "2    35\n",
       "3    40\n",
       "4    45\n",
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     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "<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>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.08438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.08112</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   Al2O3  TiO2  Density\n",
       "0   0.00   0.0  1.05696\n",
       "1   0.00   0.0  1.04158\n",
       "2   0.05   0.0  1.08438\n",
       "3   0.05   0.0  1.08112\n",
       "4   0.05   0.0  1.07781"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "0    30\n",
       "1    55\n",
       "2    25\n",
       "3    30\n",
       "4    35\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": 29,
   "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": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2421b_level0_col0\" class=\"col_heading level0 col0\" >MSE_train</th>\n",
       "      <th id=\"T_2421b_level0_col1\" class=\"col_heading level0 col1\" >MSE_test</th>\n",
       "      <th id=\"T_2421b_level0_col2\" class=\"col_heading level0 col2\" >MAE_train</th>\n",
       "      <th id=\"T_2421b_level0_col3\" class=\"col_heading level0 col3\" >MAE_test</th>\n",
       "      <th id=\"T_2421b_level0_col4\" class=\"col_heading level0 col4\" >R2_train</th>\n",
       "      <th id=\"T_2421b_level0_col5\" class=\"col_heading level0 col5\" >R2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row0\" class=\"row_heading level0 row0\" >linear_poly</th>\n",
       "      <td id=\"T_2421b_row0_col0\" class=\"data row0 col0\" >0.302768</td>\n",
       "      <td id=\"T_2421b_row0_col1\" class=\"data row0 col1\" >0.203293</td>\n",
       "      <td id=\"T_2421b_row0_col2\" class=\"data row0 col2\" >0.419467</td>\n",
       "      <td id=\"T_2421b_row0_col3\" class=\"data row0 col3\" >0.392687</td>\n",
       "      <td id=\"T_2421b_row0_col4\" class=\"data row0 col4\" >0.998860</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
       "      <td id=\"T_2421b_row1_col0\" class=\"data row1 col0\" >9.693323</td>\n",
       "      <td id=\"T_2421b_row1_col1\" class=\"data row1 col1\" >10.875442</td>\n",
       "      <td id=\"T_2421b_row1_col2\" class=\"data row1 col2\" >2.544944</td>\n",
       "      <td id=\"T_2421b_row1_col3\" class=\"data row1 col3\" >2.718424</td>\n",
       "      <td id=\"T_2421b_row1_col4\" class=\"data row1 col4\" >0.963492</td>\n",
       "      <td id=\"T_2421b_row1_col5\" class=\"data row1 col5\" >0.949019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row2\" class=\"row_heading level0 row2\" >linear</th>\n",
       "      <td id=\"T_2421b_row2_col0\" class=\"data row2 col0\" >10.468503</td>\n",
       "      <td id=\"T_2421b_row2_col1\" class=\"data row2 col1\" >14.820315</td>\n",
       "      <td id=\"T_2421b_row2_col2\" class=\"data row2 col2\" >2.657476</td>\n",
       "      <td id=\"T_2421b_row2_col3\" class=\"data row2 col3\" >2.930229</td>\n",
       "      <td id=\"T_2421b_row2_col4\" class=\"data row2 col4\" >0.960572</td>\n",
       "      <td id=\"T_2421b_row2_col5\" class=\"data row2 col5\" >0.930526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row3\" class=\"row_heading level0 row3\" >decision_tree</th>\n",
       "      <td id=\"T_2421b_row3_col0\" class=\"data row3 col0\" >10.526316</td>\n",
       "      <td id=\"T_2421b_row3_col1\" class=\"data row3 col1\" >47.426471</td>\n",
       "      <td id=\"T_2421b_row3_col2\" class=\"data row3 col2\" >1.842105</td>\n",
       "      <td id=\"T_2421b_row3_col3\" class=\"data row3 col3\" >5.735294</td>\n",
       "      <td id=\"T_2421b_row3_col4\" class=\"data row3 col4\" >0.960355</td>\n",
       "      <td id=\"T_2421b_row3_col5\" class=\"data row3 col5\" >0.777676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_2421b_row4_col0\" class=\"data row4 col0\" >20.243876</td>\n",
       "      <td id=\"T_2421b_row4_col1\" class=\"data row4 col1\" >54.501240</td>\n",
       "      <td id=\"T_2421b_row4_col2\" class=\"data row4 col2\" >3.592953</td>\n",
       "      <td id=\"T_2421b_row4_col3\" class=\"data row4 col3\" >6.598133</td>\n",
       "      <td id=\"T_2421b_row4_col4\" class=\"data row4 col4\" >0.923755</td>\n",
       "      <td id=\"T_2421b_row4_col5\" class=\"data row4 col5\" >0.744512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row5\" class=\"row_heading level0 row5\" >knn</th>\n",
       "      <td id=\"T_2421b_row5_col0\" class=\"data row5 col0\" >174.100430</td>\n",
       "      <td id=\"T_2421b_row5_col1\" class=\"data row5 col1\" >191.176471</td>\n",
       "      <td id=\"T_2421b_row5_col2\" class=\"data row5 col2\" >10.808271</td>\n",
       "      <td id=\"T_2421b_row5_col3\" class=\"data row5 col3\" >11.680672</td>\n",
       "      <td id=\"T_2421b_row5_col4\" class=\"data row5 col4\" >0.344285</td>\n",
       "      <td id=\"T_2421b_row5_col5\" class=\"data row5 col5\" >0.103812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2421b_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
       "      <td id=\"T_2421b_row6_col0\" class=\"data row6 col0\" >243.364664</td>\n",
       "      <td id=\"T_2421b_row6_col1\" class=\"data row6 col1\" >199.601477</td>\n",
       "      <td id=\"T_2421b_row6_col2\" class=\"data row6 col2\" >13.472724</td>\n",
       "      <td id=\"T_2421b_row6_col3\" class=\"data row6 col3\" >12.396799</td>\n",
       "      <td id=\"T_2421b_row6_col4\" class=\"data row6 col4\" >0.083415</td>\n",
       "      <td id=\"T_2421b_row6_col5\" class=\"data row6 col5\" >0.064317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16867d550>"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- Density <= 1.04\n",
      "|   |--- Density <= 1.03\n",
      "|   |   |--- value: [70.00]\n",
      "|   |--- Density >  1.03\n",
      "|   |   |--- Density <= 1.04\n",
      "|   |   |   |--- value: [65.00]\n",
      "|   |   |--- Density >  1.04\n",
      "|   |   |   |--- value: [60.00]\n",
      "|--- Density >  1.04\n",
      "|   |--- Density <= 1.07\n",
      "|   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |--- Density <= 1.05\n",
      "|   |   |   |   |   |--- Density <= 1.05\n",
      "|   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |   |   |   |--- Density >  1.05\n",
      "|   |   |   |   |   |   |--- value: [42.50]\n",
      "|   |   |   |   |--- Density >  1.05\n",
      "|   |   |   |   |   |--- Density <= 1.06\n",
      "|   |   |   |   |   |   |--- value: [35.00]\n",
      "|   |   |   |   |   |--- Density >  1.06\n",
      "|   |   |   |   |   |   |--- value: [22.50]\n",
      "|   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |--- Density <= 1.06\n",
      "|   |   |   |   |   |--- Density <= 1.05\n",
      "|   |   |   |   |   |   |--- value: [70.00]\n",
      "|   |   |   |   |   |--- Density >  1.05\n",
      "|   |   |   |   |   |   |--- value: [65.00]\n",
      "|   |   |   |   |--- Density >  1.06\n",
      "|   |   |   |   |   |--- Density <= 1.07\n",
      "|   |   |   |   |   |   |--- value: [55.00]\n",
      "|   |   |   |   |   |--- Density >  1.07\n",
      "|   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |--- TiO2 >  0.03\n",
      "|   |   |   |--- Density <= 1.06\n",
      "|   |   |   |   |--- value: [70.00]\n",
      "|   |   |   |--- Density >  1.06\n",
      "|   |   |   |   |--- Density <= 1.06\n",
      "|   |   |   |   |   |--- value: [65.00]\n",
      "|   |   |   |   |--- Density >  1.06\n",
      "|   |   |   |   |   |--- value: [60.00]\n",
      "|   |--- Density >  1.07\n",
      "|   |   |--- Density <= 1.12\n",
      "|   |   |   |--- Density <= 1.08\n",
      "|   |   |   |   |--- Density <= 1.07\n",
      "|   |   |   |   |   |--- value: [45.00]\n",
      "|   |   |   |   |--- Density >  1.07\n",
      "|   |   |   |   |   |--- Density <= 1.08\n",
      "|   |   |   |   |   |   |--- value: [40.00]\n",
      "|   |   |   |   |   |--- Density >  1.08\n",
      "|   |   |   |   |   |   |--- value: [35.00]\n",
      "|   |   |   |--- Density >  1.08\n",
      "|   |   |   |   |--- Density <= 1.09\n",
      "|   |   |   |   |   |--- value: [30.00]\n",
      "|   |   |   |   |--- Density >  1.09\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [22.50]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [20.00]\n",
      "|   |   |--- Density >  1.12\n",
      "|   |   |   |--- Density <= 1.18\n",
      "|   |   |   |   |--- Density <= 1.15\n",
      "|   |   |   |   |   |--- value: [70.00]\n",
      "|   |   |   |   |--- Density >  1.15\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.15\n",
      "|   |   |   |   |   |   |--- value: [65.00]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.15\n",
      "|   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |   |--- Density >  1.18\n",
      "|   |   |   |   |--- Al2O3 <= 0.15\n",
      "|   |   |   |   |   |--- Density <= 1.20\n",
      "|   |   |   |   |   |   |--- value: [50.00]\n",
      "|   |   |   |   |   |--- Density >  1.20\n",
      "|   |   |   |   |   |   |--- value: [30.00]\n",
      "|   |   |   |   |--- Al2O3 >  0.15\n",
      "|   |   |   |   |   |--- Density <= 1.18\n",
      "|   |   |   |   |   |   |--- value: [30.00]\n",
      "|   |   |   |   |   |--- Density >  1.18\n",
      "|   |   |   |   |   |   |--- value: [22.50]\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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(model, open(\"data/temp_density_tree.model.sav\", \"wb\"))"
   ]
  }
 ],
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