HYPERPARAMETER_OPTIMIZATION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-optimize Hyperparameter optimisation notebook \n",
    "[Main notebook link](https://scikit-optimize.github.io/stable/auto_examples/hyperparameter-optimization.html)\n",
    "\n",
    "* [Scikit-optimize](https://scikit-optimize.github.io/stable/)\n",
    "* [HyperOpt](https://github.com/hyperopt/hyperopt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "boston = load_boston()\n",
    "X, y = boston.data, boston.target\n",
    "n_features = X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gradient boosted trees tend to do well on problems like this\n",
    "reg = GradientBoostingRegressor(n_estimators=50, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.fit(X_train, y_train)\n",
    "y_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.861 | RMSE: 3.119\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 574,
       "width": 592
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
    "ax.scatter(y_test, y_pred, s=100)\n",
    "ax.plot(y_test, y_test, linewidth=3.5, color='k')\n",
    "\n",
    "_r2 = r2_score(y_test, y_pred)\n",
    "_rmse = np.sqrt( mean_squared_error(y_test, y_pred) )\n",
    "print('R2: {0:0.3f} | RMSE: {1:0.3f}'.format(_r2, _rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ## CROSS-VALIDATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "def display_scores(scores, metric):\n",
    "    #print(\"Scores:\", scores)\n",
    "    score_key = 'test_{}'.format(metric)\n",
    "    print(metric)\n",
    "    print(\"Mean:\", scores[score_key].mean())\n",
    "    print(\"Standard deviation:\", scores[score_key].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "_scoring = [\"neg_mean_squared_error\"]\n",
    "forest_scores = cross_validate(reg, X_train, y_train, scoring=_scoring, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg_mean_squared_error\n",
      "Mean: -12.30979411248526\n",
      "Standard deviation: 6.438051642251604\n"
     ]
    }
   ],
   "source": [
    "display_scores(forest_scores, \"neg_mean_squared_error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ### Hyperparameter search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from dask.distributed import Client\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:50888</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>17.18 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:50888' processes=4 threads=8, memory=17.18 GB>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_reg = GradientBoostingRegressor(random_state=42)\n",
    "param_grid = { \n",
    "    'min_samples_leaf': [2, 5, 10, 100],\n",
    "    'min_samples_split': [2, 5, 10, 100],\n",
    "    'n_estimators': [100, 200, 400, 700],\n",
    "    'max_depth': [5,10,15,20]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "CV_rfr = RandomizedSearchCV(estimator=RF_reg, n_iter=10, param_distributions=param_grid, scoring=\"neg_mean_squared_error\", cv= 5, verbose=1, n_jobs=-1, refit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend DaskDistributedBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    6.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 904 ms, sys: 100 ms, total: 1 s\n",
      "Wall time: 6.28 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "with joblib.parallel_backend('dask'):\n",
    "    CV_rfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 100, 'min_samples_split': 100, 'min_samples_leaf': 5, 'max_depth': 20}\n"
     ]
    }
   ],
   "source": [
    "print(CV_rfr.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_n_estimators</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.274650</td>\n",
       "      <td>0.053841</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 100...</td>\n",
       "      <td>-8.671213</td>\n",
       "      <td>-15.289582</td>\n",
       "      <td>-10.374475</td>\n",
       "      <td>-8.687919</td>\n",
       "      <td>-15.072023</td>\n",
       "      <td>-11.619043</td>\n",
       "      <td>2.974090</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.477478</td>\n",
       "      <td>0.029693</td>\n",
       "      <td>0.001845</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 100...</td>\n",
       "      <td>-8.401892</td>\n",
       "      <td>-15.424825</td>\n",
       "      <td>-8.321938</td>\n",
       "      <td>-9.096511</td>\n",
       "      <td>-16.935994</td>\n",
       "      <td>-11.636232</td>\n",
       "      <td>3.750642</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.561276</td>\n",
       "      <td>0.072511</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>{'n_estimators': 700, 'min_samples_split': 10,...</td>\n",
       "      <td>-8.822571</td>\n",
       "      <td>-16.990815</td>\n",
       "      <td>-9.008891</td>\n",
       "      <td>-10.817529</td>\n",
       "      <td>-14.450938</td>\n",
       "      <td>-12.018149</td>\n",
       "      <td>3.204831</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.412332</td>\n",
       "      <td>0.026131</td>\n",
       "      <td>0.002115</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 5, ...</td>\n",
       "      <td>-10.234256</td>\n",
       "      <td>-24.301139</td>\n",
       "      <td>-9.872093</td>\n",
       "      <td>-10.908084</td>\n",
       "      <td>-18.425869</td>\n",
       "      <td>-14.748288</td>\n",
       "      <td>5.721582</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.560921</td>\n",
       "      <td>0.223534</td>\n",
       "      <td>0.003291</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>{'n_estimators': 400, 'min_samples_split': 2, ...</td>\n",
       "      <td>-10.471399</td>\n",
       "      <td>-24.433166</td>\n",
       "      <td>-10.317219</td>\n",
       "      <td>-11.374612</td>\n",
       "      <td>-18.095469</td>\n",
       "      <td>-14.938373</td>\n",
       "      <td>5.552075</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.019774</td>\n",
       "      <td>0.044190</td>\n",
       "      <td>0.002798</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 2, ...</td>\n",
       "      <td>-10.095146</td>\n",
       "      <td>-19.497136</td>\n",
       "      <td>-11.246545</td>\n",
       "      <td>-17.691678</td>\n",
       "      <td>-17.670148</td>\n",
       "      <td>-15.240131</td>\n",
       "      <td>3.806751</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.935529</td>\n",
       "      <td>0.051472</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 2, ...</td>\n",
       "      <td>-9.923940</td>\n",
       "      <td>-19.651046</td>\n",
       "      <td>-11.229205</td>\n",
       "      <td>-17.900744</td>\n",
       "      <td>-17.577677</td>\n",
       "      <td>-15.256523</td>\n",
       "      <td>3.907614</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.104271</td>\n",
       "      <td>0.012265</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 5, ...</td>\n",
       "      <td>-26.314055</td>\n",
       "      <td>-29.064783</td>\n",
       "      <td>-40.558458</td>\n",
       "      <td>-26.370188</td>\n",
       "      <td>-34.330570</td>\n",
       "      <td>-31.327611</td>\n",
       "      <td>5.460009</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.100944</td>\n",
       "      <td>0.023998</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 10,...</td>\n",
       "      <td>-26.314055</td>\n",
       "      <td>-29.064783</td>\n",
       "      <td>-40.558458</td>\n",
       "      <td>-26.370188</td>\n",
       "      <td>-34.330570</td>\n",
       "      <td>-31.327611</td>\n",
       "      <td>5.460009</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.082421</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>0.000750</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 5, ...</td>\n",
       "      <td>-26.314055</td>\n",
       "      <td>-29.064783</td>\n",
       "      <td>-40.558458</td>\n",
       "      <td>-26.370188</td>\n",
       "      <td>-34.330570</td>\n",
       "      <td>-31.327611</td>\n",
       "      <td>5.460009</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "8       0.274650      0.053841         0.001172        0.000254   \n",
       "7       0.477478      0.029693         0.001845        0.000511   \n",
       "1       1.561276      0.072511         0.002847        0.000252   \n",
       "3       0.412332      0.026131         0.002115        0.001273   \n",
       "6       1.560921      0.223534         0.003291        0.000463   \n",
       "2       1.019774      0.044190         0.002798        0.000188   \n",
       "0       0.935529      0.051472         0.002642        0.000519   \n",
       "4       0.104271      0.012265         0.000832        0.000134   \n",
       "5       0.100944      0.023998         0.000724        0.000050   \n",
       "9       0.082421      0.007593         0.000750        0.000139   \n",
       "\n",
       "  param_n_estimators param_min_samples_split param_min_samples_leaf  \\\n",
       "8                100                     100                      5   \n",
       "7                200                     100                     10   \n",
       "1                700                      10                      2   \n",
       "3                100                       5                      5   \n",
       "6                400                       2                      5   \n",
       "2                200                       2                      2   \n",
       "0                200                       2                      2   \n",
       "4                100                       5                    100   \n",
       "5                100                      10                    100   \n",
       "9                100                       5                    100   \n",
       "\n",
       "  param_max_depth                                             params  \\\n",
       "8              20  {'n_estimators': 100, 'min_samples_split': 100...   \n",
       "7              15  {'n_estimators': 200, 'min_samples_split': 100...   \n",
       "1               5  {'n_estimators': 700, 'min_samples_split': 10,...   \n",
       "3              20  {'n_estimators': 100, 'min_samples_split': 5, ...   \n",
       "6              15  {'n_estimators': 400, 'min_samples_split': 2, ...   \n",
       "2              20  {'n_estimators': 200, 'min_samples_split': 2, ...   \n",
       "0              15  {'n_estimators': 200, 'min_samples_split': 2, ...   \n",
       "4              15  {'n_estimators': 100, 'min_samples_split': 5, ...   \n",
       "5               5  {'n_estimators': 100, 'min_samples_split': 10,...   \n",
       "9              10  {'n_estimators': 100, 'min_samples_split': 5, ...   \n",
       "\n",
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "8          -8.671213         -15.289582         -10.374475          -8.687919   \n",
       "7          -8.401892         -15.424825          -8.321938          -9.096511   \n",
       "1          -8.822571         -16.990815          -9.008891         -10.817529   \n",
       "3         -10.234256         -24.301139          -9.872093         -10.908084   \n",
       "6         -10.471399         -24.433166         -10.317219         -11.374612   \n",
       "2         -10.095146         -19.497136         -11.246545         -17.691678   \n",
       "0          -9.923940         -19.651046         -11.229205         -17.900744   \n",
       "4         -26.314055         -29.064783         -40.558458         -26.370188   \n",
       "5         -26.314055         -29.064783         -40.558458         -26.370188   \n",
       "9         -26.314055         -29.064783         -40.558458         -26.370188   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "8         -15.072023       -11.619043        2.974090                1  \n",
       "7         -16.935994       -11.636232        3.750642                2  \n",
       "1         -14.450938       -12.018149        3.204831                3  \n",
       "3         -18.425869       -14.748288        5.721582                4  \n",
       "6         -18.095469       -14.938373        5.552075                5  \n",
       "2         -17.670148       -15.240131        3.806751                6  \n",
       "0         -17.577677       -15.256523        3.907614                7  \n",
       "4         -34.330570       -31.327611        5.460009                8  \n",
       "5         -34.330570       -31.327611        5.460009                8  \n",
       "9         -34.330570       -31.327611        5.460009                8  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(CV_rfr.cv_results_).sort_values('rank_test_score', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = CV_rfr.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=20, min_samples_leaf=5,\n",
       "                          min_samples_split=100, random_state=42)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.871 | RMSE: 3.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 574,
       "width": 592
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
    "ax.scatter(y_test, y_pred, s=100)\n",
    "ax.plot(y_test, y_test, linewidth=3.5, color='k')\n",
    "\n",
    "_r2 = r2_score(y_test, y_pred)\n",
    "_rmse = np.sqrt( mean_squared_error(y_test, y_pred) )\n",
    "print('R2: {0:0.3f} | RMSE: {1:0.3f}'.format(_r2, _rmse))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from skopt.space import Real, Integer\n",
    "from skopt.utils import use_named_args\n",
    "\n",
    "\n",
    "# The list of hyper-parameters we want to optimize. For each one we define the\n",
    "# bounds, the corresponding scikit-learn parameter name, as well as how to\n",
    "# sample values from that dimension (`'log-uniform'` for the learning rate)\n",
    "space  = [Integer(1, 5, name='max_depth'),\n",
    "          Real(10**-5, 10**0, \"log-uniform\", name='learning_rate'),\n",
    "          Integer(1, n_features, name='max_features'),\n",
    "          Integer(2, 100, name='min_samples_split'),\n",
    "          Integer(1, 100, name='min_samples_leaf')]\n",
    "\n",
    "# this decorator allows your objective function to receive a the parameters as\n",
    "# keyword arguments. This is particularly convenient when you want to set\n",
    "# scikit-learn estimator parameters\n",
    "\n",
    "@use_named_args(space)\n",
    "def objective(**params):\n",
    "    reg.set_params(**params)\n",
    "    return -np.mean(cross_val_score(reg, X, y, cv=5, n_jobs=-1,\n",
    "                                    scoring=\"neg_mean_absolute_error\"))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from skopt import gp_minimize\n",
    "res_gp = gp_minimize(objective, space, n_calls=50, random_state=0)\n",
    "\n",
    "\"Best score=%.4f\" % res_gp.fun\n",
    "res_gp"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(\"\"\"Best parameters:\n",
    "- max_depth=%d\n",
    "- learning_rate=%.6f\n",
    "- max_features=%d\n",
    "- min_samples_split=%d\n",
    "- min_samples_leaf=%d\"\"\" % (res_gp.x[0], res_gp.x[1],\n",
    "                            res_gp.x[2], res_gp.x[3],\n",
    "                            res_gp.x[4]))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from skopt.plots import plot_convergence\n",
    "\n",
    "plot_convergence(res_gp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def report(results, n_top=3):\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean validation score: {0:.3f} (std: {1:.3f})\"\n",
    "                  .format(results['mean_test_score'][candidate],\n",
    "                          results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Same thing but with BayesSearchCV\n",
    "from skopt import BayesSearchCV\n",
    "import time as time\n",
    "import scipy.stats as stats\n",
    "from skopt.space import Real, Integer\n",
    "from skopt.utils import use_named_args\n",
    "from dask.distributed import Client\n",
    "import joblib\n",
    "\n",
    "params  = {\n",
    "        'max_depth':Integer(1, 5),\n",
    "        'learning_rate':Real(10**-5, 10**0, prior=\"log-uniform\"),\n",
    "        'max_features':Integer(1, n_features),\n",
    "        'min_samples_split':Integer(2, 100),\n",
    "        'min_samples_leaf':Integer(1, 100)\n",
    "        }\n",
    "\n",
    "search = BayesSearchCV(\n",
    "    reg,\n",
    "    params,\n",
    "    n_iter=50,\n",
    "    cv=5,\n",
    "    n_jobs=1,\n",
    "    random_state=0)\n",
    "\n",
    "start = time.time()\n",
    "with joblib.parallel_backend('dask'):\n",
    "    search.fit(X, y)\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\" % (time.time() - start, len(search.cv_results_['params'])))\n",
    "report(search.cv_results_)\n",
    "best_model = search.best_params_\n",
    "print('Best model')\n",
    "print(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_Bayes_best_model = GradientBoostingRegressor(random_state=42, **best_model)\n",
    "_Bayes_best_model.fit(X_train, y_train)\n",
    "y_pred_bayes = _Bayes_best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
    "ax.scatter(y_test, y_pred_bayes, s=100)\n",
    "ax.plot(y_test, y_test, linewidth=3.5, color='k')\n",
    "\n",
    "_r2 = r2_score(y_test, y_pred_bayes)\n",
    "_rmse = np.sqrt( mean_squared_error(y_test, y_pred_bayes) )\n",
    "print('R2: {0:0.3f} | RMSE: {1:0.3f}'.format(_r2, _rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skopt.plots import plot_convergence\n",
    "plot_convergence(search)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(search.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
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 },
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