12_gradient_boosting_machines/03_sklearn_gbm_tuning_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of the GBM GridSearchCV results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file illustrates how to evaluate the [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) results for sklearn's [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) obtained after first running `sklearn_gbm_tuning.py` in this directory to test various hyperparameter combinations and store the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.145170Z",
     "start_time": "2021-02-25T02:25:07.140098Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.164754Z",
     "start_time": "2021-02-25T02:25:07.146543Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "import os\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import graphviz\n",
    "\n",
    "from statsmodels.api import OLS, add_constant\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.172832Z",
     "start_time": "2021-02-25T02:25:07.166101Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")\n",
    "np.random.seed(42)\n",
    "pd.options.display.float_format = '{:,.4f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.212026Z",
     "start_time": "2021-02-25T02:25:07.175213Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('data/tuning_sklearn_gbm.h5') as store:\n",
    "    test_feature_data = store['holdout/features']\n",
    "    test_features = test_feature_data.columns\n",
    "    test_target = store['holdout/target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GBM GridsearchCV with sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need OneStepTimeSeriesSplit because stored GridSearchCV result expects it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.217207Z",
     "start_time": "2021-02-25T02:25:07.213428Z"
    }
   },
   "outputs": [],
   "source": [
    "class OneStepTimeSeriesSplit:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to first run `sklearn_gbm_tuning.py` to perform gridsearch and store result (not included due to file size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.349524Z",
     "start_time": "2021-02-25T02:25:07.218651Z"
    }
   },
   "outputs": [],
   "source": [
    "gridsearch_result = joblib.load('results/sklearn_gbm_gridsearch.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GridSearchCV object has several additional attributes after completion that we can access after loading the pickled result to learn which hyperparameter combination performed best and its average cross-validation AUC score, which results in a modest improvement over the default values. This is shown in the following code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Parameters & AUC Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.355301Z",
     "start_time": "2021-02-25T02:25:07.350876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "learning_rate             0.0100\n",
       "max_depth                 9.0000\n",
       "max_features              1.0000\n",
       "min_impurity_decrease     0.0100\n",
       "min_samples_split        50.0000\n",
       "n_estimators            300.0000\n",
       "subsample                 1.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(gridsearch_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.366942Z",
     "start_time": "2021-02-25T02:25:07.356416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.5569'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{gridsearch_result.best_score_:.4f}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test on hold-out set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.376005Z",
     "start_time": "2021-02-25T02:25:07.367845Z"
    }
   },
   "outputs": [],
   "source": [
    "best_model = gridsearch_result.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.386243Z",
     "start_time": "2021-02-25T02:25:07.377242Z"
    }
   },
   "outputs": [],
   "source": [
    "idx = pd.IndexSlice\n",
    "test_dates = sorted(test_feature_data.index.get_level_values('date').unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.627221Z",
     "start_time": "2021-02-25T02:25:07.387058Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "auc = {}\n",
    "for i, test_date in enumerate(test_dates):\n",
    "    test_data = test_feature_data.loc[idx[:, test_date], :]\n",
    "    preds = best_model.predict(test_data)\n",
    "    auc[i] = roc_auc_score(y_true=test_target.loc[test_data.index], y_score=preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.631947Z",
     "start_time": "2021-02-25T02:25:07.628472Z"
    }
   },
   "outputs": [],
   "source": [
    "auc = pd.Series(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.643447Z",
     "start_time": "2021-02-25T02:25:07.632945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   0.5381\n",
       "1   0.5163\n",
       "2   0.5257\n",
       "3   0.5061\n",
       "4   0.5235\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:07.865626Z",
     "start_time": "2021-02-25T02:25:07.645620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = auc.sort_index(ascending=False).plot.barh(xlim=(.45, .55),\n",
    "                                               title=f'Test AUC: {auc.mean():.2%}',\n",
    "                                               figsize=(8, 4))\n",
    "ax.axvline(auc.mean(), ls='--', lw=1, c='k')\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect global feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.123609Z",
     "start_time": "2021-02-25T02:25:07.866815Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.Series(best_model.feature_importances_,\n",
    "           index=test_features)\n",
    " .sort_values()\n",
    " .tail(25)\n",
    " .plot.barh(figsize=(8, 5)))\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CV Train-Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.141964Z",
     "start_time": "2021-02-25T02:25:08.125609Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 40 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   mean_fit_time                576 non-null    float64\n",
      " 1   std_fit_time                 576 non-null    float64\n",
      " 2   mean_score_time              576 non-null    float64\n",
      " 3   std_score_time               576 non-null    float64\n",
      " 4   param_learning_rate          576 non-null    object \n",
      " 5   param_max_depth              576 non-null    object \n",
      " 6   param_max_features           576 non-null    object \n",
      " 7   param_min_impurity_decrease  576 non-null    object \n",
      " 8   param_min_samples_split      576 non-null    object \n",
      " 9   param_n_estimators           576 non-null    object \n",
      " 10  param_subsample              576 non-null    object \n",
      " 11  split0_test_score            576 non-null    float64\n",
      " 12  split1_test_score            576 non-null    float64\n",
      " 13  split2_test_score            576 non-null    float64\n",
      " 14  split3_test_score            576 non-null    float64\n",
      " 15  split4_test_score            576 non-null    float64\n",
      " 16  split5_test_score            576 non-null    float64\n",
      " 17  split6_test_score            576 non-null    float64\n",
      " 18  split7_test_score            576 non-null    float64\n",
      " 19  split8_test_score            576 non-null    float64\n",
      " 20  split9_test_score            576 non-null    float64\n",
      " 21  split10_test_score           576 non-null    float64\n",
      " 22  split11_test_score           576 non-null    float64\n",
      " 23  mean_test_score              576 non-null    float64\n",
      " 24  std_test_score               576 non-null    float64\n",
      " 25  rank_test_score              576 non-null    int32  \n",
      " 26  split0_train_score           576 non-null    float64\n",
      " 27  split1_train_score           576 non-null    float64\n",
      " 28  split2_train_score           576 non-null    float64\n",
      " 29  split3_train_score           576 non-null    float64\n",
      " 30  split4_train_score           576 non-null    float64\n",
      " 31  split5_train_score           576 non-null    float64\n",
      " 32  split6_train_score           576 non-null    float64\n",
      " 33  split7_train_score           576 non-null    float64\n",
      " 34  split8_train_score           576 non-null    float64\n",
      " 35  split9_train_score           576 non-null    float64\n",
      " 36  split10_train_score          576 non-null    float64\n",
      " 37  split11_train_score          576 non-null    float64\n",
      " 38  mean_train_score             576 non-null    float64\n",
      " 39  std_train_score              576 non-null    float64\n",
      "dtypes: float64(32), int32(1), object(7)\n",
      "memory usage: 177.9+ KB\n"
     ]
    }
   ],
   "source": [
    "results = pd.DataFrame(gridsearch_result.cv_results_).drop('params', axis=1)\n",
    "results.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.162270Z",
     "start_time": "2021-02-25T02:25:08.143770Z"
    }
   },
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_min_impurity_decrease</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>...</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>split10_train_score</th>\n",
       "      <th>split11_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0867</td>\n",
       "      <td>1.1143</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6734</td>\n",
       "      <td>0.6724</td>\n",
       "      <td>0.6715</td>\n",
       "      <td>0.6711</td>\n",
       "      <td>0.6773</td>\n",
       "      <td>0.6722</td>\n",
       "      <td>0.6774</td>\n",
       "      <td>0.6814</td>\n",
       "      <td>0.6721</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.4386</td>\n",
       "      <td>1.3369</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6654</td>\n",
       "      <td>0.6744</td>\n",
       "      <td>0.6748</td>\n",
       "      <td>0.6706</td>\n",
       "      <td>0.6847</td>\n",
       "      <td>0.6762</td>\n",
       "      <td>0.6747</td>\n",
       "      <td>0.6786</td>\n",
       "      <td>0.6730</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.3189</td>\n",
       "      <td>3.4796</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6850</td>\n",
       "      <td>0.6867</td>\n",
       "      <td>0.6885</td>\n",
       "      <td>0.6864</td>\n",
       "      <td>0.6889</td>\n",
       "      <td>0.6895</td>\n",
       "      <td>0.6943</td>\n",
       "      <td>0.6958</td>\n",
       "      <td>0.6865</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.7252</td>\n",
       "      <td>5.0023</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6852</td>\n",
       "      <td>0.6855</td>\n",
       "      <td>0.6921</td>\n",
       "      <td>0.6896</td>\n",
       "      <td>0.6874</td>\n",
       "      <td>0.6857</td>\n",
       "      <td>0.6937</td>\n",
       "      <td>0.6964</td>\n",
       "      <td>0.6870</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.9732</td>\n",
       "      <td>1.5380</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6671</td>\n",
       "      <td>0.6807</td>\n",
       "      <td>0.6759</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>0.6707</td>\n",
       "      <td>0.6767</td>\n",
       "      <td>0.6830</td>\n",
       "      <td>0.6789</td>\n",
       "      <td>0.6736</td>\n",
       "      <td>0.0061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        27.0867        1.1143           0.0076          0.0007   \n",
       "1        31.4386        1.3369           0.0074          0.0006   \n",
       "2        81.3189        3.4796           0.0118          0.0009   \n",
       "3        93.7252        5.0023           0.0115          0.0006   \n",
       "4        26.9732        1.5380           0.0073          0.0004   \n",
       "\n",
       "  param_learning_rate param_max_depth param_max_features  \\\n",
       "0                0.01               3               sqrt   \n",
       "1                0.01               3               sqrt   \n",
       "2                0.01               3               sqrt   \n",
       "3                0.01               3               sqrt   \n",
       "4                0.01               3               sqrt   \n",
       "\n",
       "  param_min_impurity_decrease param_min_samples_split param_n_estimators  ...  \\\n",
       "0                           0                      10                100  ...   \n",
       "1                           0                      10                100  ...   \n",
       "2                           0                      10                300  ...   \n",
       "3                           0                      10                300  ...   \n",
       "4                           0                      50                100  ...   \n",
       "\n",
       "  split4_train_score  split5_train_score  split6_train_score  \\\n",
       "0             0.6734              0.6724              0.6715   \n",
       "1             0.6654              0.6744              0.6748   \n",
       "2             0.6850              0.6867              0.6885   \n",
       "3             0.6852              0.6855              0.6921   \n",
       "4             0.6671              0.6807              0.6759   \n",
       "\n",
       "   split7_train_score  split8_train_score  split9_train_score  \\\n",
       "0              0.6711              0.6773              0.6722   \n",
       "1              0.6706              0.6847              0.6762   \n",
       "2              0.6864              0.6889              0.6895   \n",
       "3              0.6896              0.6874              0.6857   \n",
       "4              0.6752              0.6707              0.6767   \n",
       "\n",
       "   split10_train_score  split11_train_score  mean_train_score  std_train_score  \n",
       "0               0.6774               0.6814            0.6721           0.0049  \n",
       "1               0.6747               0.6786            0.6730           0.0059  \n",
       "2               0.6943               0.6958            0.6865           0.0052  \n",
       "3               0.6937               0.6964            0.6870           0.0050  \n",
       "4               0.6830               0.6789            0.6736           0.0061  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get parameter values & mean test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.176540Z",
     "start_time": "2021-02-25T02:25:08.163594Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 8 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   learning_rate          576 non-null    object \n",
      " 1   max_depth              576 non-null    object \n",
      " 2   max_features           576 non-null    object \n",
      " 3   min_impurity_decrease  576 non-null    object \n",
      " 4   min_samples_split      576 non-null    object \n",
      " 5   n_estimators           576 non-null    object \n",
      " 6   subsample              576 non-null    object \n",
      " 7   test_score             576 non-null    float64\n",
      "dtypes: float64(1), object(7)\n",
      "memory usage: 36.1+ KB\n"
     ]
    }
   ],
   "source": [
    "test_scores = results.filter(like='param').join(results[['mean_test_score']])\n",
    "test_scores = test_scores.rename(columns={c: '_'.join(c.split('_')[1:]) for c in test_scores.columns})\n",
    "test_scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.186715Z",
     "start_time": "2021-02-25T02:25:08.179533Z"
    }
   },
   "outputs": [],
   "source": [
    "params = test_scores.columns[:-1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.204189Z",
     "start_time": "2021-02-25T02:25:08.188393Z"
    }
   },
   "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>test_score</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>max_depth</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>max_features</td>\n",
       "      <td>sqrt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>min_impurity_decrease</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>min_samples_split</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_score              parameter value\n",
       "0      0.5151          learning_rate  0.01\n",
       "1      0.5151              max_depth     3\n",
       "2      0.5151           max_features  sqrt\n",
       "3      0.5151  min_impurity_decrease     0\n",
       "4      0.5151      min_samples_split    10"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores = test_scores.set_index('test_score').stack().reset_index()\n",
    "test_scores.columns= ['test_score', 'parameter', 'value']\n",
    "test_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.216390Z",
     "start_time": "2021-02-25T02:25:08.206029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4032 entries, 0 to 4031\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   test_score  4032 non-null   float64\n",
      " 1   parameter   4032 non-null   object \n",
      " 2   value       4032 non-null   object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test_scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.220634Z",
     "start_time": "2021-02-25T02:25:08.218260Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_test_scores(df):\n",
    "    \"\"\"Select parameter values and test scores\"\"\"\n",
    "    data = df.filter(like='param').join(results[['mean_test_score']])\n",
    "    return data.rename(columns={c: '_'.join(c.split('_')[1:]) for c in data.columns})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Test Scores vs Parameter Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GridSearchCV result stores the average cross-validation scores so that we can analyze how different hyperparameter settings affect the outcome.\n",
    "\n",
    "The six seaborn swarm plots below show the distribution of AUC test scores for all parameter values. In this case, the highest AUC  test scores required a low learning_rate and a large value for max_features. Some parameter settings, such as a low learning_rate, produce a wide range of outcomes that depend on the complementary settings of other parameters. Other parameters are compatible with high scores for all settings use in the experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:08.245642Z",
     "start_time": "2021-02-25T02:25:08.221605Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   learning_rate      576 non-null    object \n",
      " 1   max_depth          576 non-null    object \n",
      " 2   max_features       576 non-null    object \n",
      " 3   min_samples_split  576 non-null    object \n",
      " 4   n_estimators       576 non-null    object \n",
      " 5   subsample          576 non-null    object \n",
      " 6   test_score         576 non-null    float64\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 31.6+ KB\n"
     ]
    }
   ],
   "source": [
    "plot_data = get_test_scores(results).drop('min_impurity_decrease', axis=1)\n",
    "plot_params = plot_data.columns[:-1].tolist()\n",
    "plot_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:10.931407Z",
     "start_time": "2021-02-25T02:25:08.247484Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(12, 6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, param in enumerate(plot_params):\n",
    "    sns.swarmplot(x=param, y='test_score', data=plot_data, ax=axes[i])\n",
    "    \n",
    "fig.suptitle('Mean Test Score Distribution by Hyperparameter', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.94)\n",
    "fig.savefig('sklearn_cv_scores_by_param', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy-encode parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:10.953272Z",
     "start_time": "2021-02-25T02:25:10.932498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   test_score                  576 non-null    float64\n",
      " 1   learning_rate_0.01          576 non-null    uint8  \n",
      " 2   learning_rate_0.1           576 non-null    uint8  \n",
      " 3   learning_rate_0.2           576 non-null    uint8  \n",
      " 4   max_depth_3                 576 non-null    uint8  \n",
      " 5   max_depth_6                 576 non-null    uint8  \n",
      " 6   max_depth_9                 576 non-null    uint8  \n",
      " 7   max_depth_12                576 non-null    uint8  \n",
      " 8   max_features_0.8            576 non-null    uint8  \n",
      " 9   max_features_1              576 non-null    uint8  \n",
      " 10  max_features_sqrt           576 non-null    uint8  \n",
      " 11  min_impurity_decrease_0.0   576 non-null    uint8  \n",
      " 12  min_impurity_decrease_0.01  576 non-null    uint8  \n",
      " 13  min_samples_split_10        576 non-null    uint8  \n",
      " 14  min_samples_split_50        576 non-null    uint8  \n",
      " 15  n_estimators_100            576 non-null    uint8  \n",
      " 16  n_estimators_300            576 non-null    uint8  \n",
      " 17  subsample_0.8               576 non-null    uint8  \n",
      " 18  subsample_1.0               576 non-null    uint8  \n",
      "dtypes: float64(1), uint8(18)\n",
      "memory usage: 14.8 KB\n"
     ]
    }
   ],
   "source": [
    "data = get_test_scores(results)\n",
    "params = data.columns[:-1].tolist()\n",
    "data = pd.get_dummies(data,columns=params, drop_first=False)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Regression Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now explore how hyperparameter settings jointly affect the mean cross-validation score. To gain insight into how parameter settings interact, we can train a DecisionTreeRegressor with the mean test score as the outcome and the parameter settings, encoded as categorical variables in one-hot or dummy format. \n",
    "\n",
    "The tree structure highlights that using all features (max_features_1), a low learning_rate, and a max_depth over three led to the best results, as shown in the following diagram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:10.957522Z",
     "start_time": "2021-02-25T02:25:10.955159Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_tree = DecisionTreeRegressor(criterion='mse',\n",
    "                                 splitter='best',\n",
    "                                 max_depth=4,\n",
    "                                 min_samples_split=5,\n",
    "                                 min_samples_leaf=10,\n",
    "                                 min_weight_fraction_leaf=0.0,\n",
    "                                 max_features=None,\n",
    "                                 random_state=42,\n",
    "                                 max_leaf_nodes=None,\n",
    "                                 min_impurity_decrease=0.0,\n",
    "                                 min_impurity_split=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:10.973002Z",
     "start_time": "2021-02-25T02:25:10.958622Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=4, min_samples_leaf=10, min_samples_split=5,\n",
       "                      random_state=42)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_features = data.drop('test_score', axis=1).columns\n",
    "reg_tree.fit(X=data[gbm_features], y=data.test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:10.982235Z",
     "start_time": "2021-02-25T02:25:10.974039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32877771, 0.        , 0.00539535, 0.26611295, 0.0109397 ,\n",
       "       0.        , 0.00227883, 0.02879713, 0.23131014, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.12280906,\n",
       "       0.        , 0.        , 0.00357913])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:11.118521Z",
     "start_time": "2021-02-25T02:25:10.984790Z"
    }
   },
   "outputs": [
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       "<!-- 9&#45;&gt;13 -->\n",
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       "<!-- 11 -->\n",
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       "<text text-anchor=\"start\" x=\"482.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<!-- 10&#45;&gt;11 -->\n",
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       "<text text-anchor=\"start\" x=\"598.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<title>14</title>\n",
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       "<text text-anchor=\"start\" x=\"714.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<text text-anchor=\"start\" x=\"830.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<title>13&#45;&gt;15</title>\n",
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       "<title>17</title>\n",
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       "<text text-anchor=\"start\" x=\"1077\" y=\"-245.8\" font-family=\"helvetica\" font-size=\"14.00\">learning_rate_0.01 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1120.5\" y=\"-230.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<!-- 24 -->\n",
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       "<title>24</title>\n",
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       "<text text-anchor=\"start\" x=\"1367\" y=\"-245.8\" font-family=\"helvetica\" font-size=\"14.00\">learning_rate_0.01 ≤ 0.5</text>\n",
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       "<!-- 16&#45;&gt;24 -->\n",
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       "<title>16&#45;&gt;24</title>\n",
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       "<title>18</title>\n",
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       "<text text-anchor=\"start\" x=\"1010.5\" y=\"-141.8\" font-family=\"helvetica\" font-size=\"14.00\">max_depth_12 ≤ 0.5</text>\n",
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       "<text text-anchor=\"start\" x=\"1035.5\" y=\"-96.8\" font-family=\"helvetica\" font-size=\"14.00\">value = 0.54</text>\n",
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       "<title>21</title>\n",
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       "<text text-anchor=\"start\" x=\"1170\" y=\"-141.8\" font-family=\"helvetica\" font-size=\"14.00\">max_depth_6 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1198.5\" y=\"-126.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<!-- 17&#45;&gt;21 -->\n",
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       "<title>17&#45;&gt;21</title>\n",
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       "<!-- 19 -->\n",
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       "<title>19</title>\n",
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       "<text text-anchor=\"start\" x=\"946.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<!-- 18&#45;&gt;19 -->\n",
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       "<title>18&#45;&gt;19</title>\n",
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       "<!-- 20 -->\n",
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       "<title>20</title>\n",
       "<path fill=\"#eb9c64\" stroke=\"black\" d=\"M1130,-53C1130,-53 1056,-53 1056,-53 1050,-53 1044,-47 1044,-41 1044,-41 1044,-12 1044,-12 1044,-6 1050,0 1056,0 1056,0 1130,0 1130,0 1136,0 1142,-6 1142,-12 1142,-12 1142,-41 1142,-41 1142,-47 1136,-53 1130,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1062.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<!-- 18&#45;&gt;20 -->\n",
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       "<title>18&#45;&gt;20</title>\n",
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       "<title>22</title>\n",
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       "<title>21&#45;&gt;22</title>\n",
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       "<title>23</title>\n",
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       "<text text-anchor=\"start\" x=\"1294.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<title>21&#45;&gt;23</title>\n",
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       "<title>25</title>\n",
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       "<text text-anchor=\"start\" x=\"1377.5\" y=\"-141.8\" font-family=\"helvetica\" font-size=\"14.00\">subsample_1.0 ≤ 0.5</text>\n",
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       "<title>24&#45;&gt;25</title>\n",
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       "<title>28</title>\n",
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       "<text text-anchor=\"start\" x=\"1410.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-22.8\" font-family=\"helvetica\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-7.8\" font-family=\"helvetica\" font-size=\"14.00\">value = 0.537</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>25&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1441,-88.95C1441,-80.72 1441,-71.85 1441,-63.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1444.5,-63.24 1441,-53.24 1437.5,-63.24 1444.5,-63.24\"/>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>27</title>\n",
       "<path fill=\"#e99558\" stroke=\"black\" d=\"M1594,-53C1594,-53 1520,-53 1520,-53 1514,-53 1508,-47 1508,-41 1508,-41 1508,-12 1508,-12 1508,-6 1514,0 1520,0 1520,0 1594,0 1594,0 1600,0 1606,-6 1606,-12 1606,-12 1606,-41 1606,-41 1606,-47 1600,-53 1594,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1526.5\" y=\"-37.8\" font-family=\"helvetica\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1516\" y=\"-22.8\" font-family=\"helvetica\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"1516\" y=\"-7.8\" font-family=\"helvetica\" font-size=\"14.00\">value = 0.541</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>25&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1481.57,-88.95C1493.25,-79.43 1505.97,-69.07 1517.58,-59.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1519.87,-62.27 1525.41,-53.24 1515.44,-56.84 1519.87,-62.27\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f6188e03b20>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_file = 'results/gbm_sklearn_tree.dot'\n",
    "dot_data = export_graphviz(reg_tree,\n",
    "                          out_file=out_file,\n",
    "                          feature_names=gbm_features,\n",
    "                          max_depth=4,\n",
    "                          filled=True,\n",
    "                          rounded=True,\n",
    "                          special_characters=True)\n",
    "if out_file is not None:\n",
    "    dot_data = Path(out_file).read_text()\n",
    "\n",
    "graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T13:40:08.199563Z",
     "start_time": "2018-10-23T13:40:08.196698Z"
    }
   },
   "source": [
    "Overfit regression tree to learn detailed rules that classify all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:23.189671Z",
     "start_time": "2021-02-25T02:25:23.178012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree = DecisionTreeRegressor(criterion='mse',\n",
    "                                 splitter='best',\n",
    "                                 min_samples_split=2,\n",
    "                                 min_samples_leaf=1,\n",
    "                                 min_weight_fraction_leaf=0.0,\n",
    "                                 max_features=None,\n",
    "                                 random_state=42,\n",
    "                                 max_leaf_nodes=None,\n",
    "                                 min_impurity_decrease=0.0,\n",
    "                                 min_impurity_split=None)\n",
    "\n",
    "gbm_features = data.drop('test_score', axis=1).columns\n",
    "reg_tree.fit(X=data[gbm_features], y=data.test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart below displays the influence of the hyperparameter settings in producing different outcomes, measured by their feature importance for a decision tree that is grown to its maximum depth. Naturally, the features that appear near the top of the tree also accumulate the highest importance scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:23.941277Z",
     "start_time": "2021-02-25T02:25:23.684238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_fi = (pd.Series(reg_tree.feature_importances_, \n",
    "                    index=gbm_features)\n",
    "          .sort_values(ascending=False))\n",
    "gbm_fi = gbm_fi[gbm_fi > 0]\n",
    "idx = [p.split('_') for p in gbm_fi.index]\n",
    "gbm_fi.index = ['_'.join(p[:-1]) + '=' + p[-1] for p in idx]\n",
    "gbm_fi.sort_values().plot.barh(figsize=(5,5))\n",
    "plt.title('Hyperparameter Importance')\n",
    "sns.despine()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use a linear regression to gain insights into the statistical significance of the linear relationship between hyperparameters and test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T02:25:26.902003Z",
     "start_time": "2021-02-25T02:25:26.870554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             test_score   R-squared:                       0.403\n",
      "Model:                            OLS   Adj. R-squared:                  0.391\n",
      "Method:                 Least Squares   F-statistic:                     26.95\n",
      "Date:                Wed, 24 Feb 2021   Prob (F-statistic):           3.34e-45\n",
      "Time:                        20:25:26   Log-Likelihood:                 1976.0\n",
      "No. Observations:                 576   AIC:                            -3928.\n",
      "Df Residuals:                     564   BIC:                            -3876.\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:                  HC3                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                          0.5213      0.002    322.640      0.000       0.518       0.524\n",
      "learning_rate_0.1              0.0080      0.001      9.293      0.000       0.006       0.010\n",
      "learning_rate_0.2              0.0061      0.001      6.807      0.000       0.004       0.008\n",
      "max_depth_6                    0.0043      0.001      4.333      0.000       0.002       0.006\n",
      "max_depth_9                    0.0071      0.001      6.781      0.000       0.005       0.009\n",
      "max_depth_12                   0.0061      0.001      5.545      0.000       0.004       0.008\n",
      "max_features_1                 0.0081      0.001      9.428      0.000       0.006       0.010\n",
      "max_features_sqrt             -0.0016      0.001     -1.873      0.061      -0.003    7.37e-05\n",
      "min_impurity_decrease_0.01 -6.906e-05      0.001     -0.104      0.917      -0.001       0.001\n",
      "min_samples_split_50           0.0007      0.001      0.997      0.319      -0.001       0.002\n",
      "n_estimators_300               0.0042      0.001      6.278      0.000       0.003       0.005\n",
      "subsample_1.0                  0.0006      0.001      0.845      0.398      -0.001       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       15.164   Durbin-Watson:                   0.971\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.665\n",
      "Skew:                          -0.398   Prob(JB):                     0.000397\n",
      "Kurtosis:                       3.139   Cond. No.                         7.90\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC3)\n"
     ]
    }
   ],
   "source": [
    "data = get_test_scores(results)\n",
    "params = data.columns[:-1].tolist()\n",
    "data = pd.get_dummies(data,columns=params, drop_first=True)\n",
    "\n",
    "model = OLS(endog=data.test_score, exog=add_constant(data.drop('test_score', axis=1))).fit(cov_type='HC3')\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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