day-5-machine-learning-part-II/04 - Gradient Boosting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:08:48.235646Z",
     "start_time": "2019-07-04T13:08:48.228802Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:08:32.554326Z",
     "start_time": "2019-07-04T13:08:30.294323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy on training set: 1.000000\n",
      "accuracy on test set: 0.958042\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, random_state=0)\n",
    "\n",
    "gbrt = GradientBoostingClassifier(random_state=0)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "print(\"accuracy on training set: %f\" % gbrt.score(X_train, y_train))\n",
    "print(\"accuracy on test set: %f\" % gbrt.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:08:32.634775Z",
     "start_time": "2019-07-04T13:08:32.557222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy on training set: 0.990610\n",
      "accuracy on test set: 0.972028\n"
     ]
    }
   ],
   "source": [
    "gbrt = GradientBoostingClassifier(random_state=0, max_depth=1)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "print(\"accuracy on training set: %f\" % gbrt.score(X_train, y_train))\n",
    "print(\"accuracy on test set: %f\" % gbrt.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:08:32.850864Z",
     "start_time": "2019-07-04T13:08:32.637861Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy on training set: 0.988263\n",
      "accuracy on test set: 0.965035\n"
     ]
    }
   ],
   "source": [
    "gbrt = GradientBoostingClassifier(random_state=0, learning_rate=0.01)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "print(\"accuracy on training set: %f\" % gbrt.score(X_train, y_train))\n",
    "print(\"accuracy on test set: %f\" % gbrt.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:08:33.320646Z",
     "start_time": "2019-07-04T13:08:32.856641Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbrt = GradientBoostingClassifier(random_state=0, max_depth=1)\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "plt.barh(range(cancer.data.shape[1]), gbrt.feature_importances_)\n",
    "plt.yticks(range(cancer.data.shape[1]), cancer.feature_names);\n",
    "ax = plt.gca()\n",
    "ax.set_position([0.4, .2, .9, .9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "Use GradientBoostingRegressor on the Bike dataset.\n",
    "Search over the ``learning_rate`` and ``max_depth`` using ``GridSearchCV``.\n",
    "What happens if you change ``n_estimators``?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:10:12.555027Z",
     "start_time": "2019-07-04T13:10:12.409453Z"
    }
   },
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  income  \n",
       "0             0              40   United-States   <=50K  \n",
       "1             0              13   United-States   <=50K  \n",
       "2             0              40   United-States   <=50K  \n",
       "3             0              40   United-States   <=50K  \n",
       "4             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('data/adult.csv',index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:10:14.667063Z",
     "start_time": "2019-07-04T13:10:14.425496Z"
    }
   },
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  \n",
       "0             0              40   United-States  \n",
       "1             0              13   United-States  \n",
       "2             0              40   United-States  \n",
       "3             0              40   United-States  \n",
       "4             0              40            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income = data.income\n",
    "data_features = data.drop(\"income\", axis=1)\n",
    "display(data_features.head())\n",
    "\n",
    "### one hot encode data\n",
    "data_one_hot = pd.get_dummies(data_features)\n",
    "data_one_hot.head()\n",
    "\n",
    "### Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_one_hot, income)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:38:25.657674Z",
     "start_time": "2019-07-04T13:37:39.332373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8661748256887494\n"
     ]
    }
   ],
   "source": [
    "### Cross-validation with default parameters\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "scores = cross_val_score(GradientBoostingClassifier(), X_train_scaled, y_train, cv=5)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:43:01.745308Z",
     "start_time": "2019-07-04T13:43:01.738592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              n_iter_no_change=None, presort='auto', random_state=None,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:48:25.180211Z",
     "start_time": "2019-07-04T13:48:25.171361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.31622777, 0.4216965 , 0.56234133, 0.74989421, 1.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-0.5,0,5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:48:24.835250Z",
     "start_time": "2019-07-04T13:47:31.323745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sampl...      subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'learning_rate': [0.1, 0.3]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'learning_rate': [0.1,0.3]}\n",
    "grid = GridSearchCV(GradientBoostingClassifier(random_state=0), \n",
    "                    param_grid=param_grid,\n",
    "                    cv=5,\n",
    "                   return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:48:25.168374Z",
     "start_time": "2019-07-04T13:48:24.838619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'param_learning_rate')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores.plot(x='param_learning_rate', y='mean_train_score', yerr='std_train_score', ax=plt.gca())\n",
    "scores.plot(x='param_learning_rate', y='mean_test_score', yerr='std_test_score', ax=plt.gca())\n",
    "plt.ylabel('Score',fontsize=20)\n",
    "plt.xlabel('param_learning_rate',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:55:02.147376Z",
     "start_time": "2019-07-04T13:49:46.411507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sampl...      subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'max_depth': [2, 5, 10]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth': [2,5,10]}\n",
    "grid = GridSearchCV(GradientBoostingClassifier(random_state=0), \n",
    "                    param_grid=param_grid,\n",
    "                    cv=5,\n",
    "                   return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:55:02.595277Z",
     "start_time": "2019-07-04T13:55:02.363634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'param_max_depth')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores.plot(x='param_max_depth', y='mean_train_score', yerr='std_train_score', ax=plt.gca())\n",
    "scores.plot(x='param_max_depth', y='mean_test_score', yerr='std_test_score', ax=plt.gca())\n",
    "plt.ylabel('Score',fontsize=20)\n",
    "plt.xlabel('param_max_depth',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:55:12.134916Z",
     "start_time": "2019-07-04T13:55:02.598388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sampl...      subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [2, 5, 10]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'n_estimators': [2,5,10]}\n",
    "grid = GridSearchCV(GradientBoostingClassifier(random_state=0), \n",
    "                    param_grid=param_grid,\n",
    "                    cv=5,\n",
    "                   return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:55:12.423131Z",
     "start_time": "2019-07-04T13:55:12.137504Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'param_n_estimators')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores.plot(x='param_n_estimators', y='mean_train_score', yerr='std_train_score', ax=plt.gca())\n",
    "scores.plot(x='param_n_estimators', y='mean_test_score', yerr='std_test_score', ax=plt.gca())\n",
    "plt.ylabel('Score',fontsize=20)\n",
    "plt.xlabel('param_n_estimators',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.8"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}