try_lightfGBM_cv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# In this file, trying lightGBM with cross validation\n",
    "# try the package in scikit-learn too\n",
    "# single thread\n",
    "\n",
    "# lgb document: http://lightgbm.readthedocs.io/en/latest/python/lightgbm.html#lightgbm-package\n",
    "# xgb document: http://xgboost.readthedocs.io/en/latest/python/python_api.html\n",
    "\n",
    "# LightGBM scikit-learn API: http://lightgbm.readthedocs.io/en/latest/python/lightgbm.html#scikit-learn-api\n",
    "# XGBoost scikit-learn API: http://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn\n",
    "# both scikit-learn lightGBM, Xgboost provide regressor and classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from pandas import Series, DataFrame \n",
    "import lightgbm as lgb \n",
    "import xgboost as xgb \n",
    "from datetime import datetime \n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "from lightgbm.sklearn import LGBMClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.grid_search import GridSearchCV \n",
    "from xgboost import plot_importance\n",
    "from lightgbm import plot_importance as lgb_plot_importance\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</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>sex</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>77516</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",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</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",
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       "      <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>215646</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>234721</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>338409</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  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_Status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country  Income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('adult.csv',header=None) \n",
    "# assign column names to the data\n",
    "data.columns=['age','workclass','fnlwgt','education','education-num','marital_Status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country','Income'] \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24720\n",
       "1     7841\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data preprocessing\n",
    "\n",
    "## encode label\n",
    "l=LabelEncoder() \n",
    "l.fit(data.Income) \n",
    "l.classes_ \n",
    "data.Income=Series(l.transform(data.Income))\n",
    "data.Income.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## convert categorical data into one-hot, and drop original categorical data\n",
    "one_hot_workclass=pd.get_dummies(data.workclass) \n",
    "one_hot_education=pd.get_dummies(data.education) \n",
    "one_hot_marital_Status=pd.get_dummies(data.marital_Status) \n",
    "one_hot_occupation=pd.get_dummies(data.occupation)\n",
    "one_hot_relationship=pd.get_dummies(data.relationship) \n",
    "one_hot_race=pd.get_dummies(data.race) \n",
    "one_hot_sex=pd.get_dummies(data.sex) \n",
    "one_hot_native_country=pd.get_dummies(data.native_country) \n",
    "\n",
    "data.drop(['workclass','education','marital_Status','occupation','relationship','race','sex','native_country'],axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Wife</th>\n",
       "      <th>Without-pay</th>\n",
       "      <th>Yugoslavia</th>\n",
       "      <th>Income</th>\n",
       "      <th>age</th>\n",
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       "      <td>9</td>\n",
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       "      <td>40</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>234721</td>\n",
       "      <td>40</td>\n",
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       "      <td>338409</td>\n",
       "      <td>40</td>\n",
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       "<p>5 rows × 107 columns</p>\n",
       "</div>"
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      "text/plain": [
       "    10th   11th   12th   1st-4th   5th-6th   7th-8th   9th   ?   Adm-clerical  \\\n",
       "0      0      0      0         0         0         0     0   0              1   \n",
       "1      0      0      0         0         0         0     0   0              0   \n",
       "2      0      0      0         0         0         0     0   0              0   \n",
       "3      0      1      0         0         0         0     0   0              0   \n",
       "4      0      0      0         0         0         0     0   0              0   \n",
       "\n",
       "    Amer-Indian-Eskimo       ...         Wife   Without-pay   Yugoslavia  \\\n",
       "0                    0       ...            0             0            0   \n",
       "1                    0       ...            0             0            0   \n",
       "2                    0       ...            0             0            0   \n",
       "3                    0       ...            0             0            0   \n",
       "4                    0       ...            1             0            0   \n",
       "\n",
       "   Income  age  capital_gain  capital_loss  education-num  fnlwgt  \\\n",
       "0       0   39          2174             0             13   77516   \n",
       "1       0   50             0             0             13   83311   \n",
       "2       0   38             0             0              9  215646   \n",
       "3       0   53             0             0              7  234721   \n",
       "4       0   28             0             0             13  338409   \n",
       "\n",
       "   hours_per_week  \n",
       "0              40  \n",
       "1              13  \n",
       "2              40  \n",
       "3              40  \n",
       "4              40  \n",
       "\n",
       "[5 rows x 107 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.concat([data,one_hot_workclass,one_hot_education,one_hot_marital_Status,one_hot_occupation,one_hot_relationship,one_hot_race,one_hot_sex,one_hot_native_country],axis=1) \n",
    "## remove dulpicate columns \n",
    "i = np.unique(data.columns, return_index=True) \n",
    "data=data.iloc[:, i[1]]  # use the index of unique columns\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = data.drop('Income',axis=1) \n",
    "label = data.Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label.mode()[0]\n",
    "label.fillna(label.mode()[0],inplace=True)  # impute missing data with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24720\n",
       "1     7841\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_train,features_test,label_train,label_test=train_test_split(features,label,test_size=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# method 1 - xgboost, with cross validation\n",
    "\n",
    "dtrain=xgb.DMatrix(features_train,label=label_train)\n",
    "dtest=xgb.DMatrix(features_test)\n",
    "## booster params\n",
    "booster_params = {'max_depth':7, 'eta':1, 'silent':1,'objective':'binary:logistic','eval_metric':'auc','learning_rate':.05}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 4)\n"
     ]
    }
   ],
   "source": [
    "num_boost_round = 50\n",
    "nfold = 5\n",
    "metrics = ('auc', 'logloss')\n",
    "seed = 410\n",
    "\n",
    "xgb_cv = xgb.cv(booster_params, dtrain, num_boost_round, nfold, metrics, seed)\n",
    "print(xgb_cv.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0.850824</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.859200</td>\n",
       "      <td>0.001329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.852598</td>\n",
       "      <td>0.003079</td>\n",
       "      <td>0.861530</td>\n",
       "      <td>0.001734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.853106</td>\n",
       "      <td>0.003554</td>\n",
       "      <td>0.862625</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.854285</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.863689</td>\n",
       "      <td>0.001647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.855042</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>0.864591</td>\n",
       "      <td>0.000981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.855559</td>\n",
       "      <td>0.004038</td>\n",
       "      <td>0.865549</td>\n",
       "      <td>0.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.856063</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.866360</td>\n",
       "      <td>0.001218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.856301</td>\n",
       "      <td>0.003618</td>\n",
       "      <td>0.866771</td>\n",
       "      <td>0.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.856692</td>\n",
       "      <td>0.003609</td>\n",
       "      <td>0.867315</td>\n",
       "      <td>0.001473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.857321</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>0.867708</td>\n",
       "      <td>0.001699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.857595</td>\n",
       "      <td>0.003271</td>\n",
       "      <td>0.868208</td>\n",
       "      <td>0.001508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.858033</td>\n",
       "      <td>0.003339</td>\n",
       "      <td>0.868602</td>\n",
       "      <td>0.001401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.857986</td>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.868967</td>\n",
       "      <td>0.001425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.858467</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.869444</td>\n",
       "      <td>0.001435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.858670</td>\n",
       "      <td>0.003110</td>\n",
       "      <td>0.869718</td>\n",
       "      <td>0.001458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.858907</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.870040</td>\n",
       "      <td>0.001409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.859236</td>\n",
       "      <td>0.002819</td>\n",
       "      <td>0.870416</td>\n",
       "      <td>0.001384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.859557</td>\n",
       "      <td>0.002853</td>\n",
       "      <td>0.870786</td>\n",
       "      <td>0.001360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.859745</td>\n",
       "      <td>0.002837</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>0.001378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.859865</td>\n",
       "      <td>0.002767</td>\n",
       "      <td>0.871256</td>\n",
       "      <td>0.001235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.860032</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.871565</td>\n",
       "      <td>0.001147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.860121</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0.871807</td>\n",
       "      <td>0.001133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.860308</td>\n",
       "      <td>0.002820</td>\n",
       "      <td>0.872097</td>\n",
       "      <td>0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.860543</td>\n",
       "      <td>0.002749</td>\n",
       "      <td>0.872397</td>\n",
       "      <td>0.001250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.860661</td>\n",
       "      <td>0.002844</td>\n",
       "      <td>0.872634</td>\n",
       "      <td>0.001264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.860790</td>\n",
       "      <td>0.002815</td>\n",
       "      <td>0.872966</td>\n",
       "      <td>0.001300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.860901</td>\n",
       "      <td>0.002739</td>\n",
       "      <td>0.873108</td>\n",
       "      <td>0.001322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.861142</td>\n",
       "      <td>0.002940</td>\n",
       "      <td>0.873382</td>\n",
       "      <td>0.001251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.861207</td>\n",
       "      <td>0.002992</td>\n",
       "      <td>0.873566</td>\n",
       "      <td>0.001238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.861276</td>\n",
       "      <td>0.002998</td>\n",
       "      <td>0.873715</td>\n",
       "      <td>0.001296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.861481</td>\n",
       "      <td>0.002920</td>\n",
       "      <td>0.873968</td>\n",
       "      <td>0.001331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.861652</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.874128</td>\n",
       "      <td>0.001312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.861803</td>\n",
       "      <td>0.002908</td>\n",
       "      <td>0.874265</td>\n",
       "      <td>0.001294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.861846</td>\n",
       "      <td>0.002971</td>\n",
       "      <td>0.874481</td>\n",
       "      <td>0.001257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.862016</td>\n",
       "      <td>0.003034</td>\n",
       "      <td>0.874637</td>\n",
       "      <td>0.001257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.862111</td>\n",
       "      <td>0.003053</td>\n",
       "      <td>0.874782</td>\n",
       "      <td>0.001250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.862213</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.874917</td>\n",
       "      <td>0.001270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.862422</td>\n",
       "      <td>0.003093</td>\n",
       "      <td>0.875222</td>\n",
       "      <td>0.001211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.862444</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>0.875392</td>\n",
       "      <td>0.001263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.862572</td>\n",
       "      <td>0.003047</td>\n",
       "      <td>0.875608</td>\n",
       "      <td>0.001146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.862571</td>\n",
       "      <td>0.002961</td>\n",
       "      <td>0.875778</td>\n",
       "      <td>0.001214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.862615</td>\n",
       "      <td>0.003033</td>\n",
       "      <td>0.875890</td>\n",
       "      <td>0.001225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.862888</td>\n",
       "      <td>0.002804</td>\n",
       "      <td>0.876348</td>\n",
       "      <td>0.001343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.862918</td>\n",
       "      <td>0.002783</td>\n",
       "      <td>0.876573</td>\n",
       "      <td>0.001368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.863141</td>\n",
       "      <td>0.002715</td>\n",
       "      <td>0.876803</td>\n",
       "      <td>0.001531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.863329</td>\n",
       "      <td>0.002631</td>\n",
       "      <td>0.877183</td>\n",
       "      <td>0.001699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.863452</td>\n",
       "      <td>0.002623</td>\n",
       "      <td>0.877366</td>\n",
       "      <td>0.001735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.863598</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>0.877624</td>\n",
       "      <td>0.001805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.863634</td>\n",
       "      <td>0.002599</td>\n",
       "      <td>0.877831</td>\n",
       "      <td>0.001784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.863773</td>\n",
       "      <td>0.002626</td>\n",
       "      <td>0.878078</td>\n",
       "      <td>0.001795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test-auc-mean  test-auc-std  train-auc-mean  train-auc-std\n",
       "0        0.850824      0.003935        0.859200       0.001329\n",
       "1        0.852598      0.003079        0.861530       0.001734\n",
       "2        0.853106      0.003554        0.862625       0.001434\n",
       "3        0.854285      0.004212        0.863689       0.001647\n",
       "4        0.855042      0.004495        0.864591       0.000981\n",
       "5        0.855559      0.004038        0.865549       0.000874\n",
       "6        0.856063      0.003676        0.866360       0.001218\n",
       "7        0.856301      0.003618        0.866771       0.001364\n",
       "8        0.856692      0.003609        0.867315       0.001473\n",
       "9        0.857321      0.003379        0.867708       0.001699\n",
       "10       0.857595      0.003271        0.868208       0.001508\n",
       "11       0.858033      0.003339        0.868602       0.001401\n",
       "12       0.857986      0.003347        0.868967       0.001425\n",
       "13       0.858467      0.003098        0.869444       0.001435\n",
       "14       0.858670      0.003110        0.869718       0.001458\n",
       "15       0.858907      0.003026        0.870040       0.001409\n",
       "16       0.859236      0.002819        0.870416       0.001384\n",
       "17       0.859557      0.002853        0.870786       0.001360\n",
       "18       0.859745      0.002837        0.871000       0.001378\n",
       "19       0.859865      0.002767        0.871256       0.001235\n",
       "20       0.860032      0.002827        0.871565       0.001147\n",
       "21       0.860121      0.002718        0.871807       0.001133\n",
       "22       0.860308      0.002820        0.872097       0.001162\n",
       "23       0.860543      0.002749        0.872397       0.001250\n",
       "24       0.860661      0.002844        0.872634       0.001264\n",
       "25       0.860790      0.002815        0.872966       0.001300\n",
       "26       0.860901      0.002739        0.873108       0.001322\n",
       "27       0.861142      0.002940        0.873382       0.001251\n",
       "28       0.861207      0.002992        0.873566       0.001238\n",
       "29       0.861276      0.002998        0.873715       0.001296\n",
       "30       0.861481      0.002920        0.873968       0.001331\n",
       "31       0.861652      0.002933        0.874128       0.001312\n",
       "32       0.861803      0.002908        0.874265       0.001294\n",
       "33       0.861846      0.002971        0.874481       0.001257\n",
       "34       0.862016      0.003034        0.874637       0.001257\n",
       "35       0.862111      0.003053        0.874782       0.001250\n",
       "36       0.862213      0.003033        0.874917       0.001270\n",
       "37       0.862422      0.003093        0.875222       0.001211\n",
       "38       0.862444      0.003076        0.875392       0.001263\n",
       "39       0.862572      0.003047        0.875608       0.001146\n",
       "40       0.862571      0.002961        0.875778       0.001214\n",
       "41       0.862615      0.003033        0.875890       0.001225\n",
       "42       0.862888      0.002804        0.876348       0.001343\n",
       "43       0.862918      0.002783        0.876573       0.001368\n",
       "44       0.863141      0.002715        0.876803       0.001531\n",
       "45       0.863329      0.002631        0.877183       0.001699\n",
       "46       0.863452      0.002623        0.877366       0.001735\n",
       "47       0.863598      0.002637        0.877624       0.001805\n",
       "48       0.863634      0.002599        0.877831       0.001784\n",
       "49       0.863773      0.002626        0.878078       0.001795"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv   # it returns the optimum number of trees required (n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CV method 1 - xgboost cv() method\n",
    "\n",
    "def modelfit(alg, dtrain, predictors, useTrainCV=True, cv_folds=10, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = dtrain\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics=('auc'), early_stopping_rounds=early_stopping_rounds, seed = 410)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg_fit = alg.fit(features_train, label_train, eval_metric='auc')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(features_train)\n",
    "    dtrain_predprob = alg.predict_proba(features_train)[:,1]\n",
    "        \n",
    "    #Print model report:\n",
    "    print(\"\\nModel Report\")\n",
    "    print(\"Accuracy : %.4g\" % accuracy_score(label_train, dtrain_predictions))\n",
    "    print(\"AUC Score (Train): %f\" % roc_auc_score(label_train, dtrain_predprob))\n",
    "                    \n",
    "    plot_importance(alg_fit, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='F score', ylabel='Features', importance_type='weight', max_num_features=7, grid=True,)\n",
    "    \n",
    "    return alg_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8522\n",
      "AUC Score (Train): 0.894681\n",
      "0:00:20.832283\n"
     ]
    },
    {
     "data": {
      "image/png": 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0MzPLcDCamZllOBjNzMwyHIxmZmYZDkYzM7MMB6OZmVmGg9GsCVizZg19+/Zl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\nf/x9kUM3sq2UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd132e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "xgb1 = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=1000,  # default value here, will be changed by cvresult\n",
    " max_depth=5,\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=410)\n",
    "xgb_model = modelfit(xgb1, dtrain, features_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)  # 0:00:20.234687"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ..., 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "ypred = xgb_model.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn accuracy 0.847476712048\n",
      "accuracy:  0.847476712048\n",
      "AUC:  0.722712689445\n",
      "Precision:  0.957236402512\n",
      "Specificity:  0.777158774373\n",
      "Recall:  0.859594383775\n"
     ]
    }
   ],
   "source": [
    "accuracy_xgb = accuracy_score(label_test,ypred) \n",
    "print(\"sklearn accuracy\", accuracy_xgb)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_xgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_xgb = roc_auc_score(label_test, ypred)   \n",
    "precision_xgb = TP/(TP+FP)  \n",
    "specificity_xgb = TN/(TN+FP) \n",
    "recall_xgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_xgb)\n",
    "print(\"AUC: \", auc_score_xgb)\n",
    "print(\"Precision: \", precision_xgb)\n",
    "print(\"Specificity: \", specificity_xgb)\n",
    "print(\"Recall: \", recall_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CV method 2 - GridSearchCV() method\n",
    "## GridSearchCV params: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n",
    "\n",
    "param_set = {\n",
    " 'max_depth':[3,5,7,9],\n",
    " 'min_child_weight':[1,3,5]  # smaller min_child_weight can handle smaller class in inbalanced dataset\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=5,\n",
    " min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.86903, std: 0.00681, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       "  mean: 0.86902, std: 0.00695, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       "  mean: 0.86865, std: 0.00690, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       "  mean: 0.87415, std: 0.00686, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: 0.87371, std: 0.00721, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: 0.87281, std: 0.00723, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.87421, std: 0.00724, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       "  mean: 0.87388, std: 0.00693, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       "  mean: 0.87271, std: 0.00696, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.87298, std: 0.00649, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       "  mean: 0.87169, std: 0.00657, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       "  mean: 0.87116, std: 0.00658, params: {'max_depth': 9, 'min_child_weight': 5}],\n",
       " {'max_depth': 7, 'min_child_weight': 1},\n",
       " 0.8742110207880858)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model2 = gsearch.fit(features_train, label_train)\n",
    "xgb_model2.grid_scores_, xgb_model2.best_params_, xgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87421, std: 0.00724, params: {'gamma': 0.0},\n",
       "  mean: 0.87483, std: 0.00685, params: {'gamma': 0.1},\n",
       "  mean: 0.87472, std: 0.00677, params: {'gamma': 0.2},\n",
       "  mean: 0.87498, std: 0.00677, params: {'gamma': 0.3},\n",
       "  mean: 0.87459, std: 0.00688, params: {'gamma': 0.4}],\n",
       " {'gamma': 0.3},\n",
       " 0.8749824643981604)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modify values with optimum values and tune other params\n",
    "\n",
    "param_set = {\n",
    " 'gamma':[i/10.0 for i in range(0,5)]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model2 = gsearch.fit(features_train, label_train)\n",
    "xgb_model2.grid_scores_, xgb_model2.best_params_, xgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87401, std: 0.00714, params: {'colsample_bytree': 0.6, 'subsample': 0.6},\n",
       "  mean: 0.87436, std: 0.00757, params: {'colsample_bytree': 0.6, 'subsample': 0.7},\n",
       "  mean: 0.87529, std: 0.00698, params: {'colsample_bytree': 0.6, 'subsample': 0.8},\n",
       "  mean: 0.87563, std: 0.00686, params: {'colsample_bytree': 0.6, 'subsample': 0.9},\n",
       "  mean: 0.87269, std: 0.00743, params: {'colsample_bytree': 0.7, 'subsample': 0.6},\n",
       "  mean: 0.87321, std: 0.00703, params: {'colsample_bytree': 0.7, 'subsample': 0.7},\n",
       "  mean: 0.87498, std: 0.00677, params: {'colsample_bytree': 0.7, 'subsample': 0.8},\n",
       "  mean: 0.87539, std: 0.00634, params: {'colsample_bytree': 0.7, 'subsample': 0.9},\n",
       "  mean: 0.87269, std: 0.00743, params: {'colsample_bytree': 0.8, 'subsample': 0.6},\n",
       "  mean: 0.87321, std: 0.00703, params: {'colsample_bytree': 0.8, 'subsample': 0.7},\n",
       "  mean: 0.87498, std: 0.00677, params: {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       "  mean: 0.87539, std: 0.00634, params: {'colsample_bytree': 0.8, 'subsample': 0.9},\n",
       "  mean: 0.87201, std: 0.00715, params: {'colsample_bytree': 0.9, 'subsample': 0.6},\n",
       "  mean: 0.87258, std: 0.00667, params: {'colsample_bytree': 0.9, 'subsample': 0.7},\n",
       "  mean: 0.87378, std: 0.00724, params: {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       "  mean: 0.87453, std: 0.00683, params: {'colsample_bytree': 0.9, 'subsample': 0.9}],\n",
       " {'colsample_bytree': 0.6, 'subsample': 0.9},\n",
       " 0.8756275516740821)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modify values with optimum values and tune other params\n",
    "\n",
    "param_set = {\n",
    " 'subsample':[i/10.0 for i in range(6,10)],\n",
    " 'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0.3, subsample=0.8, colsample_bytree=0.8, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model2 = gsearch.fit(features_train, label_train)\n",
    "xgb_model2.grid_scores_, xgb_model2.best_params_, xgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.1, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.1, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.1, 'subsample': 1.0},\n",
       "  mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.2, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.2, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.2, 'subsample': 1.0},\n",
       "  mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.3, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.3, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.3, 'subsample': 1.0},\n",
       "  mean: 0.87595, std: 0.00658, params: {'colsample_bytree': 0.4, 'subsample': 0.8},\n",
       "  mean: 0.87595, std: 0.00696, params: {'colsample_bytree': 0.4, 'subsample': 0.9},\n",
       "  mean: 0.87584, std: 0.00704, params: {'colsample_bytree': 0.4, 'subsample': 1.0},\n",
       "  mean: 0.87529, std: 0.00698, params: {'colsample_bytree': 0.5, 'subsample': 0.8},\n",
       "  mean: 0.87563, std: 0.00686, params: {'colsample_bytree': 0.5, 'subsample': 0.9},\n",
       "  mean: 0.87632, std: 0.00650, params: {'colsample_bytree': 0.5, 'subsample': 1.0},\n",
       "  mean: 0.87529, std: 0.00698, params: {'colsample_bytree': 0.6, 'subsample': 0.8},\n",
       "  mean: 0.87563, std: 0.00686, params: {'colsample_bytree': 0.6, 'subsample': 0.9},\n",
       "  mean: 0.87632, std: 0.00650, params: {'colsample_bytree': 0.6, 'subsample': 1.0}],\n",
       " {'colsample_bytree': 0.1, 'subsample': 1.0},\n",
       " 0.8764095331109016)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subsample and colsample_bytree are choosing edge values, check in more detail\n",
    "\n",
    "param_set = {\n",
    " 'subsample':[0.8, 0.9, 1.0],\n",
    " 'colsample_bytree':[i/10.0 for i in range(1,7)]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0.3, subsample=0.8, colsample_bytree=0.8, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model2 = gsearch.fit(features_train, label_train)\n",
    "xgb_model2.grid_scores_, xgb_model2.best_params_, xgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.01, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.01, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.01, 'subsample': 1.0},\n",
       "  mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.03, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.03, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.03, 'subsample': 1.0},\n",
       "  mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.05, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.05, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.05, 'subsample': 1.0},\n",
       "  mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.07, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.07, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.07, 'subsample': 1.0},\n",
       "  mean: 0.87571, std: 0.00650, params: {'colsample_bytree': 0.09, 'subsample': 0.8},\n",
       "  mean: 0.87618, std: 0.00708, params: {'colsample_bytree': 0.09, 'subsample': 0.9},\n",
       "  mean: 0.87641, std: 0.00686, params: {'colsample_bytree': 0.09, 'subsample': 1.0}],\n",
       " {'colsample_bytree': 0.01, 'subsample': 1.0},\n",
       " 0.8764095331109016)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subsample and colsample_bytree are choosing edge\n",
    "\n",
    "param_set = {\n",
    " 'subsample':[0.8, 0.9, 1.0],\n",
    " 'colsample_bytree':[i/100.0 for i in range(1,10,2)]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0.3, subsample=0.8, colsample_bytree=0.8, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model2 = gsearch.fit(features_train, label_train)\n",
    "xgb_model2.grid_scores_, xgb_model2.best_params_, xgb_model2.best_score_   # got the same accuracy in fact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8452\n",
      "AUC Score (Train): 0.888306\n",
      "0:00:07.113186\n",
      "[0 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LzMwaXm1fVFywH/tWXyLiVuDWfOcwM7P6kesn35iZmX0uuDCamZlluDCamZll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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106a91828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "xgb2 = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=100,\n",
    " max_depth=7,\n",
    " min_child_weight=1,\n",
    " gamma=0.3,\n",
    " subsample=1.0,\n",
    " colsample_bytree=0.1,\n",
    " objective= 'binary:logistic',\n",
    " nthread=7,\n",
    " scale_pos_weight=1,\n",
    " seed=410)\n",
    "xgb_model2 = modelfit(xgb2, dtrain, features_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)      # 0:00:07.11318\n",
    "\n",
    "ypred = xgb_model2.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred2=xgb_model2.predict(features_test)\n",
    "ypred2[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.848397993653\n",
      "AUC:  0.709338834984\n",
      "Precision:  0.970733662969\n",
      "Specificity:  0.823813354787\n",
      "Recall:  0.851982172179\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(label_test, ypred2)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_xgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_xgb = roc_auc_score(label_test, ypred)   \n",
    "precision_xgb = TP/(TP+FP)   \n",
    "specificity_xgb = TN/(TN+FP) \n",
    "recall_xgb = TP/(TP+FN)\n",
    "print(\"accuracy: \", accuracy_xgb)\n",
    "print(\"AUC: \", auc_score_xgb)\n",
    "print(\"Precision: \", precision_xgb)\n",
    "print(\"Specificity: \", specificity_xgb)\n",
    "print(\"Recall: \", recall_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87641, std: 0.00686, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.87637, std: 0.00690, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.87621, std: 0.00699, params: {'reg_alpha': 0.1},\n",
       "  mean: 0.87540, std: 0.00711, params: {'reg_alpha': 1},\n",
       "  mean: 0.85617, std: 0.00683, params: {'reg_alpha': 100}],\n",
       " {'reg_alpha': 1e-05},\n",
       " 0.8764095331109016)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modify values with optimum values and tune other params\n",
    "\n",
    "param_set = {\n",
    " 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0.3, subsample=1.0, colsample_bytree=0.1, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model3 = gsearch.fit(features_train, label_train)\n",
    "xgb_model3.grid_scores_, xgb_model3.best_params_, xgb_model3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87641, std: 0.00686, params: {'reg_alpha': 0},\n",
       "  mean: 0.87640, std: 0.00687, params: {'reg_alpha': 0.001},\n",
       "  mean: 0.87640, std: 0.00688, params: {'reg_alpha': 0.005},\n",
       "  mean: 0.87637, std: 0.00690, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.87634, std: 0.00704, params: {'reg_alpha': 0.05}],\n",
       " {'reg_alpha': 0},\n",
       " 0.8764095331109016)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# further tune reg_alpha value\n",
    "\n",
    "param_set = {\n",
    " 'reg_alpha':[0, 0.001, 0.005, 0.01, 0.05]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0.3, subsample=1.0, colsample_bytree=0.1, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model3 = gsearch.fit(features_train, label_train)\n",
    "xgb_model3.grid_scores_, xgb_model3.best_params_, xgb_model3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8452\n",
      "AUC Score (Train): 0.888306\n",
      "0:00:07.100202\n",
      "[0 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LzMwaXm1fVFywH/tWXyLiVuDWfOcwM7P6kesn35iZmX0uuDCamZlluDCamZll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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc78588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "xgb3 = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=100,\n",
    " max_depth=7,\n",
    " min_child_weight=1,\n",
    " gamma=0.3,\n",
    " subsample=1.0,\n",
    " colsample_bytree=0.1,\n",
    " objective= 'binary:logistic',\n",
    " nthread=7,\n",
    " scale_pos_weight=1,\n",
    " reg_alpha = 0,\n",
    " seed=410)\n",
    "xgb_model3 = modelfit(xgb3, dtrain, features_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)      # 0:00:07.100202\n",
    "\n",
    "ypred = xgb_model3.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.848397993653\n",
      "AUC:  0.709338834984\n",
      "Precision:  0.970733662969\n",
      "Specificity:  0.823813354787\n",
      "Recall:  0.851982172179\n"
     ]
    }
   ],
   "source": [
    "ypred3=xgb_model3.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred3)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_xgb = (TP + TN)/(TP+FP+FN+TN)  \n",
    "auc_score_xgb = roc_auc_score(label_test, ypred)   \n",
    "precision_xgb = TP/(TP+FP)  \n",
    "specificity_xgb = TN/(TN+FP)  \n",
    "recall_xgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_xgb)\n",
    "print(\"AUC: \", auc_score_xgb)\n",
    "print(\"Precision: \", precision_xgb)\n",
    "print(\"Specificity: \", specificity_xgb)\n",
    "print(\"Recall: \", recall_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50000, std: 0.00000, params: {'scale_pos_weight': 0.0},\n",
       "  mean: 0.87003, std: 0.00723, params: {'scale_pos_weight': 0.1},\n",
       "  mean: 0.87276, std: 0.00751, params: {'scale_pos_weight': 0.2},\n",
       "  mean: 0.87382, std: 0.00722, params: {'scale_pos_weight': 0.3},\n",
       "  mean: 0.87469, std: 0.00704, params: {'scale_pos_weight': 0.4},\n",
       "  mean: 0.87489, std: 0.00709, params: {'scale_pos_weight': 0.5},\n",
       "  mean: 0.87513, std: 0.00694, params: {'scale_pos_weight': 0.6},\n",
       "  mean: 0.87550, std: 0.00716, params: {'scale_pos_weight': 0.7},\n",
       "  mean: 0.87567, std: 0.00707, params: {'scale_pos_weight': 0.8},\n",
       "  mean: 0.87593, std: 0.00715, params: {'scale_pos_weight': 0.9},\n",
       "  mean: 0.87625, std: 0.00704, params: {'scale_pos_weight': 1.0}],\n",
       " {'scale_pos_weight': 1.0},\n",
       " 0.8762515651925522)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune scale_pos_weight\n",
    "## A value greater than 0 should be used in case of high class imbalance as it helps in faster convergence.\n",
    "\n",
    "# tune learning rate\n",
    "\n",
    "param_set = {\n",
    " 'scale_pos_weight':[i/10.0 for i in range(11)]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0, subsample=1.0, colsample_bytree=0.1, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model4 = gsearch.fit(features_train, label_train)\n",
    "xgb_model4.grid_scores_, xgb_model4.best_params_, xgb_model4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.86927, std: 0.00658, params: {'learning_rate': 0.01},\n",
       "  mean: 0.87395, std: 0.00670, params: {'learning_rate': 0.05},\n",
       "  mean: 0.87625, std: 0.00704, params: {'learning_rate': 0.1}],\n",
       " {'learning_rate': 0.1},\n",
       " 0.8762515651925522)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune learning rate\n",
    "\n",
    "param_set = {\n",
    " 'learning_rate':[0.01, 0.05, 0.1]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0, subsample=1.0, colsample_bytree=0.1, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model4 = gsearch.fit(features_train, label_train)\n",
    "xgb_model4.grid_scores_, xgb_model4.best_params_, xgb_model4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87625, std: 0.00704, params: {'learning_rate': 0.1},\n",
       "  mean: 0.87670, std: 0.00762, params: {'learning_rate': 0.2},\n",
       "  mean: 0.87644, std: 0.00762, params: {'learning_rate': 0.3},\n",
       "  mean: 0.87536, std: 0.00778, params: {'learning_rate': 0.4},\n",
       "  mean: 0.87513, std: 0.00757, params: {'learning_rate': 0.5},\n",
       "  mean: 0.87383, std: 0.00772, params: {'learning_rate': 0.6},\n",
       "  mean: 0.87317, std: 0.00747, params: {'learning_rate': 0.7},\n",
       "  mean: 0.87159, std: 0.00714, params: {'learning_rate': 0.8},\n",
       "  mean: 0.87002, std: 0.00823, params: {'learning_rate': 0.9}],\n",
       " {'learning_rate': 0.2},\n",
       " 0.8766982733341692)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune learning rate\n",
    "\n",
    "param_set = {\n",
    " 'learning_rate':[i/10.0 for i in range(1,10)]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier(learning_rate =0.1, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0, subsample=1.0, colsample_bytree=0.1, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model4 = gsearch.fit(features_train, label_train)\n",
    "xgb_model4.grid_scores_, xgb_model4.best_params_, xgb_model4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87494, std: 0.00658, params: {'n_estimators': 50},\n",
       "  mean: 0.87670, std: 0.00762, params: {'n_estimators': 100},\n",
       "  mean: 0.87334, std: 0.00810, params: {'n_estimators': 500},\n",
       "  mean: 0.87127, std: 0.00784, params: {'n_estimators': 1000}],\n",
       " {'n_estimators': 100},\n",
       " 0.8766982733341692)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tune n_estimators\n",
    "\n",
    "param_set = {\n",
    " 'n_estimators':[50, 100, 500, 1000]\n",
    "}\n",
    "gsearch = GridSearchCV(estimator = XGBClassifier(learning_rate =0.2, n_estimators=100, max_depth=7,\n",
    " min_child_weight=1, gamma=0, subsample=1.0, colsample_bytree=0.1, nthread=7,\n",
    " objective= 'binary:logistic', scale_pos_weight=1, seed=410), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=7,iid=False, cv=10)\n",
    "\n",
    "xgb_model4 = gsearch.fit(features_train, label_train)\n",
    "xgb_model4.grid_scores_, xgb_model4.best_params_, xgb_model4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8499\n",
      "AUC Score (Train): 0.893772\n",
      "0:00:07.033940\n",
      "[0 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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kzSH3qeaazgWGp1/V9W3g/LT9WknzJS0gKZ7zgK8BCyTNBXoAt33MY5qZWT2o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\nJqcS/rw0vwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108f7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "xgb4 = XGBClassifier(\n",
    " learning_rate =0.2,\n",
    " n_estimators=100,\n",
    " max_depth=7,\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    " subsample=1.0,\n",
    " colsample_bytree=0.1,\n",
    " objective= 'binary:logistic',\n",
    " nthread=7,\n",
    " scale_pos_weight=1,\n",
    " reg_alpha = 0,\n",
    " seed=410)\n",
    "xgb_model4 = modelfit(xgb4, dtrain, features_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)      # 0:00:07.033940\n",
    "\n",
    "ypred = xgb_model4.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.848807452145\n",
      "AUC:  0.721758470556\n",
      "Precision:  0.960577308566\n",
      "Specificity:  0.789135096497\n",
      "Recall:  0.858781362007\n"
     ]
    }
   ],
   "source": [
    "ypred4=xgb_model4.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred4)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_xgb = (TP + TN)/(TP+FP+FN+TN)  \n",
    "auc_score_xgb = roc_auc_score(label_test, ypred) \n",
    "precision_xgb = TP/(TP+FP)   \n",
    "specificity_xgb = TN/(TN+FP)  \n",
    "recall_xgb = TP/(TP+FN)  \n",
    "print(\"accuracy: \", accuracy_xgb)\n",
    "print(\"AUC: \", auc_score_xgb)\n",
    "print(\"Precision: \", precision_xgb)\n",
    "print(\"Specificity: \", specificity_xgb)\n",
    "print(\"Recall: \", recall_xgb)\n",
    "\n",
    "# both precision and specificity dropped, while accuracy, AUC, recall increased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# LightGBM Cross Validation method 1 - cv()\n",
    "\n",
    "dtrain = lgb.Dataset(features_train,label=label_train)\n",
    "booster_params = {'num_leaves':150, 'objective':'binary','max_depth':7,'learning_rate':.05,'max_bin':200,'metric':['auc', 'binary_logloss']}\n",
    "\n",
    "lgb_cv = lgb.cv(booster_params, dtrain, num_boost_round=50, folds=None, nfold=10, stratified=False, shuffle=True, metrics=['auc', 'binary_logloss'], fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=50, fpreproc=None, verbose_eval=None, show_stdv=True, seed=410, callbacks=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_logloss-mean [0.67123043924063652, 0.65139968652314162, 0.6334090478646146, 0.61707393158234103, 0.60215633642897981, 0.5885687788400743, 0.57610061703434745, 0.56467321606002818, 0.55417778915667226, 0.54450406048468047, 0.53553570548339169, 0.52730612541052102, 0.51968403600165969, 0.51267157222319426, 0.50617885097847459, 0.50014533314886511, 0.49460702398684375, 0.48948487277231267, 0.48469406532938153, 0.48025269710269292, 0.47617989978364833, 0.47234428603754636, 0.46881724622918242, 0.46551140746888803, 0.46245355762753454, 0.45960002455070337, 0.45692524360681536, 0.45443107998963528, 0.45211543968501233, 0.44994298121316423, 0.44793565266358382, 0.44611660787129714, 0.44442305567494955, 0.44286426433432757, 0.4413440483923905, 0.43996959424570337, 0.43870635611908615, 0.43753499439706739, 0.4364244665259906, 0.43542838650051524, 0.43451267423777812, 0.43364357440729079, 0.4327928037506229, 0.43202529020641878, 0.43130508771746312, 0.43063571054875716, 0.4300286995532745, 0.42946387780903894, 0.42890551049528386, 0.42843782610392706] 50\n",
      "binary_logloss-stdv [0.00028328415358737759, 0.00053453826379939735, 0.00078273000176618706, 0.00099004927796002219, 0.0011890605770262018, 0.0013851512215012781, 0.0015438002478981433, 0.0017378630963558578, 0.0018775944425257287, 0.002038401861414507, 0.0022153506379341686, 0.0023864482853251587, 0.002576625706973543, 0.0027085075647671885, 0.0028768501900448555, 0.0030310704474634125, 0.0031790748825465194, 0.0032579681854906085, 0.0033880032857064759, 0.003520168011572151, 0.003651640236408322, 0.0037653554081151877, 0.0038760086969752048, 0.0039991625526483304, 0.0041139619237711995, 0.0042407715640244772, 0.0043170593412428741, 0.0043615486732495332, 0.0043993086354767582, 0.0044691780321400813, 0.0045674273819397895, 0.0046378723619901127, 0.0046758395999124706, 0.0047504892012108961, 0.0047762705961793168, 0.0048147805506848391, 0.004866706742645375, 0.0049290749926675023, 0.0049702237463248988, 0.0050160481262567658, 0.0050496869965718375, 0.0051138065958710576, 0.0051464597208532928, 0.0051283182689431097, 0.0051480175465320769, 0.0051751341732229773, 0.0051823743260890186, 0.0051782150663452521, 0.0052263352756093673, 0.0052443400379229132] 50\n",
      "auc-mean [0.81339606743162829, 0.81475886095027161, 0.81548401963504646, 0.81625707121070401, 0.81686530621553055, 0.81720483570644331, 0.81735173740080769, 0.81779391293975645, 0.81782637119449808, 0.81806613077747348, 0.81853884108480612, 0.81868891054513837, 0.81895149858964122, 0.81893291637630905, 0.81917124735909685, 0.81934045839226854, 0.81947252556870076, 0.81938197431018234, 0.81978402227620806, 0.81976436162072164, 0.81987189696682683, 0.82000740836535857, 0.81995952193766164, 0.82002691264527583, 0.82014216453154309, 0.82019804607193547, 0.82025746325464099, 0.82039939370981452, 0.82049623681637818, 0.82058597574990222, 0.82066483296400494, 0.82070579468590876, 0.82070248890238207, 0.82066393560418072, 0.82075417305560028, 0.82075884765076912, 0.82079559537730729, 0.82079966825532868, 0.82078033355087765, 0.8207659293155235, 0.82071666749476047, 0.82069120742533708, 0.8207183540553501, 0.82073451622519822, 0.82075899421417442, 0.82078810661120727, 0.82077745355985032, 0.82075831972200974, 0.82078127468408424, 0.82078758025374299] 50\n",
      "auc-stdv [0.0053101366538244946, 0.0051285332357901671, 0.0047738055648903739, 0.0044506601644646307, 0.0046896045828230077, 0.0045642336235625809, 0.0043489092791607186, 0.0042651665061733193, 0.0044274512151545268, 0.0044588809221426688, 0.0044686165884856915, 0.0045036152151627816, 0.0045892653705018344, 0.004286398888777969, 0.0042918669537105889, 0.0042683286420921269, 0.0045344466063449345, 0.00454920525589799, 0.0044341319191012569, 0.0045350402722859635, 0.0045184771930321752, 0.0045703987421040716, 0.0045166045865236999, 0.0045907802883623271, 0.0046316498838514771, 0.0046432893471499174, 0.0045361772140335736, 0.0044602001665305516, 0.0043596004863690916, 0.0043937795030907561, 0.0044663367905278008, 0.0044694783784131045, 0.0043899422563237391, 0.0043772163133232195, 0.0043942062629736059, 0.004339724559066443, 0.0043185446645616042, 0.0043334203917533487, 0.0043709245415575158, 0.0043686102826403653, 0.0043705181781421238, 0.0044045283100298538, 0.0044366052400509138, 0.0044436793658952306, 0.0043971001218842077, 0.0044095354027380391, 0.0044089525764597634, 0.0043918694058225211, 0.0044373247248406375, 0.0044218438981048439] 50\n"
     ]
    }
   ],
   "source": [
    "for k, v in lgb_cv.items():\n",
    "    print(k,v, len(v))\n",
    "    \n",
    "# It seems that the results are becomming better and better till 50 rounds, elts's choose 50 as n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8401\n",
      "AUC Score (Train): 0.877117\n",
      "0:00:00.408237\n"
     ]
    },
    {
     "data": {
      "image/png": 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BpBLgEGBYROwFLASermOVNsDQiChOH7vUVRTNzFqCXr16MXv2bAAeeugh9thjjwInajwt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\nzMwsw4XRzMws4/8DttcncrtGgR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1016a92e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_estimators = 50 \n",
    "\n",
    "start=datetime.now()\n",
    "\n",
    "lgb1 = LGBMClassifier(\n",
    " boosting_type='gbdt',\n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.1, n_estimators=n_estimators, max_bin=255, \n",
    " subsample_for_bin=0.8, objective=None, min_split_gain=0, \n",
    " min_child_weight=5, min_child_samples=10, subsample=1, \n",
    " subsample_freq=1, colsample_bytree=1, reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True)\n",
    "\n",
    "#Fit the algorithm on the data\n",
    "lgb_fit = lgb1.fit(features_train, label_train, eval_metric='auc')\n",
    "        \n",
    "#Predict training set:\n",
    "dtrain_predictions = lgb1.predict(features_train)\n",
    "dtrain_predprob = lgb1.predict_proba(features_train)[:,1]\n",
    "#Print model report:\n",
    "print(\"\\nModel Report\")\n",
    "print(\"Accuracy : %.4g\" % accuracy_score(label_train, dtrain_predictions))\n",
    "print(\"AUC Score (Train): %f\" % roc_auc_score(label_train, dtrain_predprob))\n",
    "     \n",
    "lgb_plot_importance(lgb_fit, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='F score', ylabel='Features', importance_type='split', max_num_features=7, grid=True,)\n",
    "    \n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)  # 0:00:00.381134, much faster. Lower accuracy, maybe because of the param tuning is limited with cv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def lgb_model_fit(alg, features_train, label_train):\n",
    "    #Fit the algorithm on the data\n",
    "    alg_fit = alg.fit(features_train, label_train, eval_metric='auc')\n",
    "\n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(features_train)\n",
    "    dtrain_predprob = alg.predict_proba(features_train)[:,1]\n",
    "    #Print model report:\n",
    "    print(\"\\nModel Report\")\n",
    "    print(\"Accuracy : %.4g\" % accuracy_score(label_train, dtrain_predictions))\n",
    "    print(\"AUC Score (Train): %f\" % roc_auc_score(label_train, dtrain_predprob))\n",
    "\n",
    "    lgb_plot_importance(alg_fit, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='F score', ylabel='Features', importance_type='split', max_num_features=7, grid=True,)\n",
    "\n",
    "    return alg_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.85892, std: 0.00829, params: {'n_estimators': 20},\n",
       "  mean: 0.86448, std: 0.00804, params: {'n_estimators': 30},\n",
       "  mean: 0.86828, std: 0.00789, params: {'n_estimators': 40},\n",
       "  mean: 0.87092, std: 0.00725, params: {'n_estimators': 50},\n",
       "  mean: 0.87237, std: 0.00714, params: {'n_estimators': 60},\n",
       "  mean: 0.87354, std: 0.00721, params: {'n_estimators': 70}],\n",
       " {'n_estimators': 70},\n",
       " 0.8735423039713573)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LightGBM cross validation method 2 - GridSearchCV()\n",
    "# the code here will be running forever\n",
    "\n",
    "param_set = {\n",
    " 'n_estimators':[20, 30, 40, 50, 60, 70]\n",
    "}\n",
    "\n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=50, max_bin=225, # choose a small values for bins to speed up\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87354, std: 0.00721, params: {'n_estimators': 70},\n",
       "  mean: 0.87516, std: 0.00699, params: {'n_estimators': 100},\n",
       "  mean: 0.87364, std: 0.00720, params: {'n_estimators': 500},\n",
       "  mean: 0.86703, std: 0.00729, params: {'n_estimators': 1000}],\n",
       " {'n_estimators': 100},\n",
       " 0.8751582381381662)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'n_estimators':[70, 100, 500, 1000]\n",
    "}\n",
    "\n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=50, max_bin=225, # choose a small values for bins to speed up\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87516, std: 0.00699, params: {'max_depth': 5},\n",
       "  mean: 0.87438, std: 0.00711, params: {'max_depth': 7},\n",
       "  mean: 0.87445, std: 0.00713, params: {'max_depth': 9},\n",
       "  mean: 0.87347, std: 0.00713, params: {'max_depth': 11},\n",
       "  mean: 0.87316, std: 0.00696, params: {'max_depth': 13},\n",
       "  mean: 0.87306, std: 0.00728, params: {'max_depth': 15}],\n",
       " {'max_depth': 5},\n",
       " 0.8751582381381662)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'max_depth':[5,7,9,11,13,15]\n",
    "}\n",
    "\n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.85743, std: 0.00786, params: {'max_depth': 1},\n",
       "  mean: 0.86739, std: 0.00779, params: {'max_depth': 2},\n",
       "  mean: 0.87217, std: 0.00725, params: {'max_depth': 3},\n",
       "  mean: 0.87448, std: 0.00670, params: {'max_depth': 4},\n",
       "  mean: 0.87516, std: 0.00699, params: {'max_depth': 5}],\n",
       " {'max_depth': 5},\n",
       " 0.8751582381381662)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'max_depth':[1,2,3,4,5]\n",
    "}\n",
    "\n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87502, std: 0.00698, params: {'num_leaves': 20},\n",
       "  mean: 0.87523, std: 0.00699, params: {'num_leaves': 25},\n",
       "  mean: 0.87516, std: 0.00699, params: {'num_leaves': 30},\n",
       "  mean: 0.87516, std: 0.00699, params: {'num_leaves': 35}],\n",
       " {'num_leaves': 25},\n",
       " 0.8752295970057501)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'num_leaves':[20, 25, 30, 35] # this value often starts with 2^max_depth\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87516, std: 0.00699, params: {'min_child_samples': 10},\n",
       "  mean: 0.87376, std: 0.00697, params: {'min_child_samples': 50},\n",
       "  mean: 0.87151, std: 0.00712, params: {'min_child_samples': 100},\n",
       "  mean: 0.87151, std: 0.00712, params: {'min_child_samples': 100}],\n",
       " {'min_child_samples': 10},\n",
       " 0.8751582381381662)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'min_child_samples':[10, 50, 100, 100] # this depends on num_leaves the number of data records. \n",
    "    # large value to avoid overfitting, but may lead to underfitting\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87516, std: 0.00699, params: {'min_child_samples': 1},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 3},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 5},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 7},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 9},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 10}],\n",
       " {'min_child_samples': 1},\n",
       " 0.8751582381381662)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'min_child_samples':[1, 3, 5, 7, 9, 10] # this depends on num_leaves the number of data records. \n",
    "    # large value to avoid overfitting, but may lead to underfitting\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier( boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_\n",
    "\n",
    "# in fact here, mean, std didn't change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87516, std: 0.00699, params: {'min_child_samples': 10},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 11},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 12},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 13},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 14},\n",
       "  mean: 0.87516, std: 0.00699, params: {'min_child_samples': 15}],\n",
       " {'min_child_samples': 10},\n",
       " 0.8751582381381662)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'min_child_samples':[10, 11, 12, 13, 14, 15] # this depends on num_leaves the number of data records. \n",
    "    # large value to avoid overfitting, but may lead to underfitting\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, objective=None, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model2 = gsearch.fit(features_train, label_train)\n",
    "lgb_model2.grid_scores_, lgb_model2.best_params_, lgb_model2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.846\n",
      "AUC Score (Train): 0.885370\n",
      "0:00:00.635665\n",
      "[0 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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YMWPYaqutOOecc9h9992RxEEHHcTBBx+czy5Yjmp6usYOEfFORKwFxqav2lgC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\nMpwYzczMMv4/dIxSHgtWvNcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120d9b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "lgb2 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.1, \n",
    " n_estimators=100, \n",
    " max_bin=225,\n",
    " subsample_for_bin=5000,\n",
    " min_split_gain=0, \n",
    " min_child_weight=5, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model2 = lgb_model_fit(lgb2, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.635665\n",
    "\n",
    "ypred = lgb_model2.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.848705087522\n",
      "AUC:  0.720628320665\n",
      "Precision:  0.961379126019\n",
      "Specificity:  0.791486291486\n",
      "Recall:  0.858165334606\n"
     ]
    }
   ],
   "source": [
    "ypred2=lgb_model2.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred2)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 100},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 500},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 10000},\n",
       "  mean: 0.87537, std: 0.00709, params: {'max_bin': 100, 'subsample_for_bin': 100},\n",
       "  mean: 0.87537, std: 0.00709, params: {'max_bin': 100, 'subsample_for_bin': 500},\n",
       "  mean: 0.87537, std: 0.00709, params: {'max_bin': 100, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87537, std: 0.00709, params: {'max_bin': 100, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87537, std: 0.00709, params: {'max_bin': 100, 'subsample_for_bin': 10000},\n",
       "  mean: 0.87495, std: 0.00723, params: {'max_bin': 200, 'subsample_for_bin': 100},\n",
       "  mean: 0.87495, std: 0.00723, params: {'max_bin': 200, 'subsample_for_bin': 500},\n",
       "  mean: 0.87495, std: 0.00723, params: {'max_bin': 200, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87495, std: 0.00723, params: {'max_bin': 200, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87495, std: 0.00723, params: {'max_bin': 200, 'subsample_for_bin': 10000},\n",
       "  mean: 0.87516, std: 0.00699, params: {'max_bin': 225, 'subsample_for_bin': 100},\n",
       "  mean: 0.87516, std: 0.00699, params: {'max_bin': 225, 'subsample_for_bin': 500},\n",
       "  mean: 0.87516, std: 0.00699, params: {'max_bin': 225, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87516, std: 0.00699, params: {'max_bin': 225, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87516, std: 0.00699, params: {'max_bin': 225, 'subsample_for_bin': 10000},\n",
       "  mean: 0.87489, std: 0.00704, params: {'max_bin': 300, 'subsample_for_bin': 100},\n",
       "  mean: 0.87489, std: 0.00704, params: {'max_bin': 300, 'subsample_for_bin': 500},\n",
       "  mean: 0.87489, std: 0.00704, params: {'max_bin': 300, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87489, std: 0.00704, params: {'max_bin': 300, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87489, std: 0.00704, params: {'max_bin': 300, 'subsample_for_bin': 10000},\n",
       "  mean: 0.87510, std: 0.00690, params: {'max_bin': 500, 'subsample_for_bin': 100},\n",
       "  mean: 0.87510, std: 0.00690, params: {'max_bin': 500, 'subsample_for_bin': 500},\n",
       "  mean: 0.87510, std: 0.00690, params: {'max_bin': 500, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87510, std: 0.00690, params: {'max_bin': 500, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87510, std: 0.00690, params: {'max_bin': 500, 'subsample_for_bin': 10000},\n",
       "  mean: 0.87508, std: 0.00702, params: {'max_bin': 1000, 'subsample_for_bin': 100},\n",
       "  mean: 0.87508, std: 0.00702, params: {'max_bin': 1000, 'subsample_for_bin': 500},\n",
       "  mean: 0.87508, std: 0.00702, params: {'max_bin': 1000, 'subsample_for_bin': 1000},\n",
       "  mean: 0.87508, std: 0.00702, params: {'max_bin': 1000, 'subsample_for_bin': 5000},\n",
       "  mean: 0.87508, std: 0.00702, params: {'max_bin': 1000, 'subsample_for_bin': 10000}],\n",
       " {'max_bin': 50, 'subsample_for_bin': 100},\n",
       " 0.8753941953241245)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'max_bin':[50, 100, 200, 225, 300, 500, 1000],\n",
    " 'subsample_for_bin':[100, 500, 1000, 5000, 10000]\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model3 = gsearch.fit(features_train, label_train)\n",
    "lgb_model3.grid_scores_, lgb_model3.best_params_, lgb_model3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.86329, std: 0.00706, params: {'max_bin': 10, 'subsample_for_bin': 20},\n",
       "  mean: 0.86329, std: 0.00706, params: {'max_bin': 10, 'subsample_for_bin': 40},\n",
       "  mean: 0.86329, std: 0.00706, params: {'max_bin': 10, 'subsample_for_bin': 60},\n",
       "  mean: 0.86329, std: 0.00706, params: {'max_bin': 10, 'subsample_for_bin': 80},\n",
       "  mean: 0.86329, std: 0.00706, params: {'max_bin': 10, 'subsample_for_bin': 100},\n",
       "  mean: 0.86834, std: 0.00695, params: {'max_bin': 20, 'subsample_for_bin': 20},\n",
       "  mean: 0.86834, std: 0.00695, params: {'max_bin': 20, 'subsample_for_bin': 40},\n",
       "  mean: 0.86834, std: 0.00695, params: {'max_bin': 20, 'subsample_for_bin': 60},\n",
       "  mean: 0.86834, std: 0.00695, params: {'max_bin': 20, 'subsample_for_bin': 80},\n",
       "  mean: 0.86834, std: 0.00695, params: {'max_bin': 20, 'subsample_for_bin': 100},\n",
       "  mean: 0.87431, std: 0.00676, params: {'max_bin': 30, 'subsample_for_bin': 20},\n",
       "  mean: 0.87431, std: 0.00676, params: {'max_bin': 30, 'subsample_for_bin': 40},\n",
       "  mean: 0.87431, std: 0.00676, params: {'max_bin': 30, 'subsample_for_bin': 60},\n",
       "  mean: 0.87431, std: 0.00676, params: {'max_bin': 30, 'subsample_for_bin': 80},\n",
       "  mean: 0.87431, std: 0.00676, params: {'max_bin': 30, 'subsample_for_bin': 100},\n",
       "  mean: 0.87468, std: 0.00705, params: {'max_bin': 40, 'subsample_for_bin': 20},\n",
       "  mean: 0.87468, std: 0.00705, params: {'max_bin': 40, 'subsample_for_bin': 40},\n",
       "  mean: 0.87468, std: 0.00705, params: {'max_bin': 40, 'subsample_for_bin': 60},\n",
       "  mean: 0.87468, std: 0.00705, params: {'max_bin': 40, 'subsample_for_bin': 80},\n",
       "  mean: 0.87468, std: 0.00705, params: {'max_bin': 40, 'subsample_for_bin': 100},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 20},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 40},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 60},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 80},\n",
       "  mean: 0.87539, std: 0.00713, params: {'max_bin': 50, 'subsample_for_bin': 100}],\n",
       " {'max_bin': 50, 'subsample_for_bin': 20},\n",
       " 0.8753941953241245)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'max_bin':[10, 20, 30, 40, 50],\n",
    " 'subsample_for_bin':[20, 40, 60, 80, 100]\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=225,\n",
    " subsample_for_bin=5000, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7, silent=True), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model3 = gsearch.fit(features_train, label_train)\n",
    "lgb_model3.grid_scores_, lgb_model3.best_params_, lgb_model3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8455\n",
      "AUC Score (Train): 0.885088\n",
      "0:00:00.622074\n",
      "[0 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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AE0g+Yg6Sj5nrA8wiKTTry/R14M2ImJBOVfYmmcqszkbpvu4Cvgf8PSI+lLRA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\n2J5GfK5ryLw+BT/X6fkqT59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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121acac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "lgb3 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.1, \n",
    " n_estimators=100, \n",
    " max_bin=50,\n",
    " subsample_for_bin=20,\n",
    " min_split_gain=0, \n",
    " min_child_weight=5, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model3 = lgb_model_fit(lgb3, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.622074\n",
    "\n",
    "ypred = lgb_model3.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.847988535162\n",
      "AUC:  0.719704880201\n",
      "Precision:  0.96084458105\n",
      "Specificity:  0.788752703677\n",
      "Recall:  0.85779050346\n"
     ]
    }
   ],
   "source": [
    "ypred3=lgb_model3.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred3)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)\n",
    "\n",
    "# the results here didn't really improve, maybe because of overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8777\n",
      "AUC Score (Train): 0.935886\n",
      "0:00:00.835169\n",
      "[1 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vky2jbgTMAjYFXgF65hda8xMRwxp7TEnH88Xyb43REXFyXf3NzKx8NPQa4R/I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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e1ab390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "lgb3 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.1, \n",
    " n_estimators=100, \n",
    " max_bin=50,\n",
    " subsample_for_bin=5000,\n",
    " min_split_gain=0, \n",
    " min_child_weight=5, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model3 = lgb_model_fit(lgb3, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.622074\n",
    "\n",
    "ypred = lgb_model3.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.870406387552\n",
      "AUC:  0.786565544728\n",
      "Precision:  0.947382586462\n",
      "Specificity:  0.789104638619\n",
      "Recall:  0.889450410613\n"
     ]
    }
   ],
   "source": [
    "ypred3=lgb_model3.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred3)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.91795, std: 0.00419, params: {'colsample_bytree': 0.2, 'min_child_weight': 3, 'subsample': 0.2},\n",
       "  mean: 0.92079, std: 0.00417, params: {'colsample_bytree': 0.2, 'min_child_weight': 3, 'subsample': 0.4},\n",
       "  mean: 0.92196, std: 0.00429, params: {'colsample_bytree': 0.2, 'min_child_weight': 3, 'subsample': 0.6},\n",
       "  mean: 0.92262, std: 0.00427, params: {'colsample_bytree': 0.2, 'min_child_weight': 3, 'subsample': 0.8},\n",
       "  mean: 0.92315, std: 0.00449, params: {'colsample_bytree': 0.2, 'min_child_weight': 3, 'subsample': 1},\n",
       "  mean: 0.91624, std: 0.00422, params: {'colsample_bytree': 0.2, 'min_child_weight': 5, 'subsample': 0.2},\n",
       "  mean: 0.91949, std: 0.00443, params: {'colsample_bytree': 0.2, 'min_child_weight': 5, 'subsample': 0.4},\n",
       "  mean: 0.92113, std: 0.00423, params: {'colsample_bytree': 0.2, 'min_child_weight': 5, 'subsample': 0.6},\n",
       "  mean: 0.92187, std: 0.00428, params: {'colsample_bytree': 0.2, 'min_child_weight': 5, 'subsample': 0.8},\n",
       "  mean: 0.92222, std: 0.00430, params: {'colsample_bytree': 0.2, 'min_child_weight': 5, 'subsample': 1},\n",
       "  mean: 0.91493, std: 0.00443, params: {'colsample_bytree': 0.2, 'min_child_weight': 7, 'subsample': 0.2},\n",
       "  mean: 0.91795, std: 0.00434, params: {'colsample_bytree': 0.2, 'min_child_weight': 7, 'subsample': 0.4},\n",
       "  mean: 0.91992, std: 0.00423, params: {'colsample_bytree': 0.2, 'min_child_weight': 7, 'subsample': 0.6},\n",
       "  mean: 0.92143, std: 0.00430, params: {'colsample_bytree': 0.2, 'min_child_weight': 7, 'subsample': 0.8},\n",
       "  mean: 0.92175, std: 0.00450, params: {'colsample_bytree': 0.2, 'min_child_weight': 7, 'subsample': 1},\n",
       "  mean: 0.91433, std: 0.00447, params: {'colsample_bytree': 0.2, 'min_child_weight': 9, 'subsample': 0.2},\n",
       "  mean: 0.91687, std: 0.00444, params: {'colsample_bytree': 0.2, 'min_child_weight': 9, 'subsample': 0.4},\n",
       "  mean: 0.91865, std: 0.00423, params: {'colsample_bytree': 0.2, 'min_child_weight': 9, 'subsample': 0.6},\n",
       "  mean: 0.92061, std: 0.00434, params: {'colsample_bytree': 0.2, 'min_child_weight': 9, 'subsample': 0.8},\n",
       "  mean: 0.92130, std: 0.00437, params: {'colsample_bytree': 0.2, 'min_child_weight': 9, 'subsample': 1},\n",
       "  mean: 0.87789, std: 0.00479, params: {'colsample_bytree': 0.4, 'min_child_weight': 3, 'subsample': 0.2},\n",
       "  mean: 0.87828, std: 0.00479, params: {'colsample_bytree': 0.4, 'min_child_weight': 3, 'subsample': 0.4},\n",
       "  mean: 0.87857, std: 0.00469, params: {'colsample_bytree': 0.4, 'min_child_weight': 3, 'subsample': 0.6},\n",
       "  mean: 0.87855, std: 0.00479, params: {'colsample_bytree': 0.4, 'min_child_weight': 3, 'subsample': 0.8},\n",
       "  mean: 0.87855, std: 0.00495, params: {'colsample_bytree': 0.4, 'min_child_weight': 3, 'subsample': 1},\n",
       "  mean: 0.87715, std: 0.00497, params: {'colsample_bytree': 0.4, 'min_child_weight': 5, 'subsample': 0.2},\n",
       "  mean: 0.87832, std: 0.00483, params: {'colsample_bytree': 0.4, 'min_child_weight': 5, 'subsample': 0.4},\n",
       "  mean: 0.87824, std: 0.00477, params: {'colsample_bytree': 0.4, 'min_child_weight': 5, 'subsample': 0.6},\n",
       "  mean: 0.87861, std: 0.00476, params: {'colsample_bytree': 0.4, 'min_child_weight': 5, 'subsample': 0.8},\n",
       "  mean: 0.87865, std: 0.00490, params: {'colsample_bytree': 0.4, 'min_child_weight': 5, 'subsample': 1},\n",
       "  mean: 0.87647, std: 0.00511, params: {'colsample_bytree': 0.4, 'min_child_weight': 7, 'subsample': 0.2},\n",
       "  mean: 0.87791, std: 0.00470, params: {'colsample_bytree': 0.4, 'min_child_weight': 7, 'subsample': 0.4},\n",
       "  mean: 0.87816, std: 0.00483, params: {'colsample_bytree': 0.4, 'min_child_weight': 7, 'subsample': 0.6},\n",
       "  mean: 0.87860, std: 0.00493, params: {'colsample_bytree': 0.4, 'min_child_weight': 7, 'subsample': 0.8},\n",
       "  mean: 0.87861, std: 0.00476, params: {'colsample_bytree': 0.4, 'min_child_weight': 7, 'subsample': 1},\n",
       "  mean: 0.87538, std: 0.00534, params: {'colsample_bytree': 0.4, 'min_child_weight': 9, 'subsample': 0.2},\n",
       "  mean: 0.87738, std: 0.00495, params: {'colsample_bytree': 0.4, 'min_child_weight': 9, 'subsample': 0.4},\n",
       "  mean: 0.87804, std: 0.00484, params: {'colsample_bytree': 0.4, 'min_child_weight': 9, 'subsample': 0.6},\n",
       "  mean: 0.87829, std: 0.00481, params: {'colsample_bytree': 0.4, 'min_child_weight': 9, 'subsample': 0.8},\n",
       "  mean: 0.87865, std: 0.00480, params: {'colsample_bytree': 0.4, 'min_child_weight': 9, 'subsample': 1},\n",
       "  mean: 0.92224, std: 0.00382, params: {'colsample_bytree': 0.6, 'min_child_weight': 3, 'subsample': 0.2},\n",
       "  mean: 0.92557, std: 0.00464, params: {'colsample_bytree': 0.6, 'min_child_weight': 3, 'subsample': 0.4},\n",
       "  mean: 0.92628, std: 0.00473, params: {'colsample_bytree': 0.6, 'min_child_weight': 3, 'subsample': 0.6},\n",
       "  mean: 0.92649, std: 0.00482, params: {'colsample_bytree': 0.6, 'min_child_weight': 3, 'subsample': 0.8},\n",
       "  mean: 0.92691, std: 0.00472, params: {'colsample_bytree': 0.6, 'min_child_weight': 3, 'subsample': 1},\n",
       "  mean: 0.92021, std: 0.00460, params: {'colsample_bytree': 0.6, 'min_child_weight': 5, 'subsample': 0.2},\n",
       "  mean: 0.92454, std: 0.00458, params: {'colsample_bytree': 0.6, 'min_child_weight': 5, 'subsample': 0.4},\n",
       "  mean: 0.92552, std: 0.00462, params: {'colsample_bytree': 0.6, 'min_child_weight': 5, 'subsample': 0.6},\n",
       "  mean: 0.92576, std: 0.00479, params: {'colsample_bytree': 0.6, 'min_child_weight': 5, 'subsample': 0.8},\n",
       "  mean: 0.92631, std: 0.00486, params: {'colsample_bytree': 0.6, 'min_child_weight': 5, 'subsample': 1},\n",
       "  mean: 0.91876, std: 0.00508, params: {'colsample_bytree': 0.6, 'min_child_weight': 7, 'subsample': 0.2},\n",
       "  mean: 0.92328, std: 0.00485, params: {'colsample_bytree': 0.6, 'min_child_weight': 7, 'subsample': 0.4},\n",
       "  mean: 0.92478, std: 0.00482, params: {'colsample_bytree': 0.6, 'min_child_weight': 7, 'subsample': 0.6},\n",
       "  mean: 0.92544, std: 0.00479, params: {'colsample_bytree': 0.6, 'min_child_weight': 7, 'subsample': 0.8},\n",
       "  mean: 0.92557, std: 0.00477, params: {'colsample_bytree': 0.6, 'min_child_weight': 7, 'subsample': 1},\n",
       "  mean: 0.91800, std: 0.00498, params: {'colsample_bytree': 0.6, 'min_child_weight': 9, 'subsample': 0.2},\n",
       "  mean: 0.92179, std: 0.00475, params: {'colsample_bytree': 0.6, 'min_child_weight': 9, 'subsample': 0.4},\n",
       "  mean: 0.92420, std: 0.00471, params: {'colsample_bytree': 0.6, 'min_child_weight': 9, 'subsample': 0.6},\n",
       "  mean: 0.92485, std: 0.00451, params: {'colsample_bytree': 0.6, 'min_child_weight': 9, 'subsample': 0.8},\n",
       "  mean: 0.92541, std: 0.00486, params: {'colsample_bytree': 0.6, 'min_child_weight': 9, 'subsample': 1},\n",
       "  mean: 0.92224, std: 0.00417, params: {'colsample_bytree': 0.8, 'min_child_weight': 3, 'subsample': 0.2},\n",
       "  mean: 0.92560, std: 0.00477, params: {'colsample_bytree': 0.8, 'min_child_weight': 3, 'subsample': 0.4},\n",
       "  mean: 0.92621, std: 0.00490, params: {'colsample_bytree': 0.8, 'min_child_weight': 3, 'subsample': 0.6},\n",
       "  mean: 0.92632, std: 0.00495, params: {'colsample_bytree': 0.8, 'min_child_weight': 3, 'subsample': 0.8},\n",
       "  mean: 0.92678, std: 0.00511, params: {'colsample_bytree': 0.8, 'min_child_weight': 3, 'subsample': 1},\n",
       "  mean: 0.92001, std: 0.00469, params: {'colsample_bytree': 0.8, 'min_child_weight': 5, 'subsample': 0.2},\n",
       "  mean: 0.92452, std: 0.00495, params: {'colsample_bytree': 0.8, 'min_child_weight': 5, 'subsample': 0.4},\n",
       "  mean: 0.92532, std: 0.00506, params: {'colsample_bytree': 0.8, 'min_child_weight': 5, 'subsample': 0.6},\n",
       "  mean: 0.92594, std: 0.00517, params: {'colsample_bytree': 0.8, 'min_child_weight': 5, 'subsample': 0.8},\n",
       "  mean: 0.92609, std: 0.00516, params: {'colsample_bytree': 0.8, 'min_child_weight': 5, 'subsample': 1},\n",
       "  mean: 0.91897, std: 0.00451, params: {'colsample_bytree': 0.8, 'min_child_weight': 7, 'subsample': 0.2},\n",
       "  mean: 0.92321, std: 0.00482, params: {'colsample_bytree': 0.8, 'min_child_weight': 7, 'subsample': 0.4},\n",
       "  mean: 0.92487, std: 0.00495, params: {'colsample_bytree': 0.8, 'min_child_weight': 7, 'subsample': 0.6},\n",
       "  mean: 0.92525, std: 0.00497, params: {'colsample_bytree': 0.8, 'min_child_weight': 7, 'subsample': 0.8},\n",
       "  mean: 0.92557, std: 0.00504, params: {'colsample_bytree': 0.8, 'min_child_weight': 7, 'subsample': 1},\n",
       "  mean: 0.91823, std: 0.00447, params: {'colsample_bytree': 0.8, 'min_child_weight': 9, 'subsample': 0.2},\n",
       "  mean: 0.92194, std: 0.00487, params: {'colsample_bytree': 0.8, 'min_child_weight': 9, 'subsample': 0.4},\n",
       "  mean: 0.92407, std: 0.00500, params: {'colsample_bytree': 0.8, 'min_child_weight': 9, 'subsample': 0.6},\n",
       "  mean: 0.92502, std: 0.00505, params: {'colsample_bytree': 0.8, 'min_child_weight': 9, 'subsample': 0.8},\n",
       "  mean: 0.92527, std: 0.00501, params: {'colsample_bytree': 0.8, 'min_child_weight': 9, 'subsample': 1},\n",
       "  mean: 0.92265, std: 0.00410, params: {'colsample_bytree': 1, 'min_child_weight': 3, 'subsample': 0.2},\n",
       "  mean: 0.92533, std: 0.00497, params: {'colsample_bytree': 1, 'min_child_weight': 3, 'subsample': 0.4},\n",
       "  mean: 0.92597, std: 0.00523, params: {'colsample_bytree': 1, 'min_child_weight': 3, 'subsample': 0.6},\n",
       "  mean: 0.92657, std: 0.00447, params: {'colsample_bytree': 1, 'min_child_weight': 3, 'subsample': 0.8},\n",
       "  mean: 0.92649, std: 0.00518, params: {'colsample_bytree': 1, 'min_child_weight': 3, 'subsample': 1},\n",
       "  mean: 0.91987, std: 0.00498, params: {'colsample_bytree': 1, 'min_child_weight': 5, 'subsample': 0.2},\n",
       "  mean: 0.92447, std: 0.00475, params: {'colsample_bytree': 1, 'min_child_weight': 5, 'subsample': 0.4},\n",
       "  mean: 0.92555, std: 0.00495, params: {'colsample_bytree': 1, 'min_child_weight': 5, 'subsample': 0.6},\n",
       "  mean: 0.92611, std: 0.00478, params: {'colsample_bytree': 1, 'min_child_weight': 5, 'subsample': 0.8},\n",
       "  mean: 0.92601, std: 0.00488, params: {'colsample_bytree': 1, 'min_child_weight': 5, 'subsample': 1},\n",
       "  mean: 0.91827, std: 0.00489, params: {'colsample_bytree': 1, 'min_child_weight': 7, 'subsample': 0.2},\n",
       "  mean: 0.92298, std: 0.00492, params: {'colsample_bytree': 1, 'min_child_weight': 7, 'subsample': 0.4},\n",
       "  mean: 0.92464, std: 0.00484, params: {'colsample_bytree': 1, 'min_child_weight': 7, 'subsample': 0.6},\n",
       "  mean: 0.92534, std: 0.00468, params: {'colsample_bytree': 1, 'min_child_weight': 7, 'subsample': 0.8},\n",
       "  mean: 0.92559, std: 0.00498, params: {'colsample_bytree': 1, 'min_child_weight': 7, 'subsample': 1},\n",
       "  mean: 0.91767, std: 0.00479, params: {'colsample_bytree': 1, 'min_child_weight': 9, 'subsample': 0.2},\n",
       "  mean: 0.92188, std: 0.00475, params: {'colsample_bytree': 1, 'min_child_weight': 9, 'subsample': 0.4},\n",
       "  mean: 0.92393, std: 0.00481, params: {'colsample_bytree': 1, 'min_child_weight': 9, 'subsample': 0.6},\n",
       "  mean: 0.92482, std: 0.00482, params: {'colsample_bytree': 1, 'min_child_weight': 9, 'subsample': 0.8},\n",
       "  mean: 0.92530, std: 0.00488, params: {'colsample_bytree': 1, 'min_child_weight': 9, 'subsample': 1}],\n",
       " {'colsample_bytree': 0.6, 'min_child_weight': 3, 'subsample': 1},\n",
       " 0.9269104641265964)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'min_child_weight':[3, 5, 7, 9],\n",
    " 'colsample_bytree':[0.2, 0.4, 0.6, 0.8, 1],\n",
    " 'subsample':[0.2, 0.4, 0.6, 0.8, 1]\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=50,\n",
    " subsample_for_bin=5000, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model4 = gsearch.fit(features_train, label_train)\n",
    "lgb_model4.grid_scores_, lgb_model4.best_params_, lgb_model4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.92680, std: 0.00506, params: {'min_child_weight': 1},\n",
       "  mean: 0.92686, std: 0.00489, params: {'min_child_weight': 2},\n",
       "  mean: 0.92649, std: 0.00518, params: {'min_child_weight': 3},\n",
       "  mean: 0.92628, std: 0.00488, params: {'min_child_weight': 4}],\n",
       " {'min_child_weight': 2},\n",
       " 0.9268573610867094)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'min_child_weight':[1,2,3,4]\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=50,\n",
    " subsample_for_bin=5000, min_split_gain=0, min_child_weight=5, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model4 = gsearch.fit(features_train, label_train)\n",
    "lgb_model4.grid_scores_, lgb_model4.best_params_, lgb_model4.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8771\n",
      "AUC Score (Train): 0.935860\n",
      "0:00:00.881916\n",
      "[1 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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eG/qXiNhd0kHAadmFteWIiDtJPjzCzMxKTFM/u3hVRLwHtJLUKiKeAIrvHdFm\nZmYtqKkz2WWSOpC8deQeSe+QfLSimZmZ1aOpM9ljgY+Ai4EpwD+BY7IKyszMrBQ0aSYbESskfQnY\nJf383G2o4zNxzczM7DNNmslK+jbwe+C2tGkH4IGsgjIzMysFTV0uvgDYD/gXQPqh89tnFZSZmVkp\naGqR/SQiPq1+IqkNjX8wvpmZ2RatqUX2SUmXk3wZ+SEk3yX7h+zCMjMzK35NLbKXAe8C84DzgUdI\nP4PXzMzM6tbg3cWSdoyI/4uItcAd6cPMzMyaoLGZbM0dxJLuzTgWMzOzktJYkVXedu8sAzEzMys1\njRXZqGfbzMzMGtFYkd1N0r8kLQcGptv/krRc0r9aIkAzs2pnn30222+/PQMGDKhpGzVqFDvssAPl\n5eWUl5fzyCOP1OwbPXo0ffr0Ydddd2Xq1KmFCNm2cA0W2YhoHRGfj4iOEdEm3a5+/vmWCnJjSRom\n6ZfNPOZxkvrlPf+xpIOb8xxmVrdhw4YxZcqU9dr/8z//kzlz5jBnzhyOPPJIAF5++WUmTJjA/Pnz\nmTJlCv/xH//BmjVrWjpk28I19Vt47DPHAX8EXgaIiJGFDWfTrFy1hl6XPVzoMDI1omw1w0o4x1LP\nb+zh7Wu2DzzwQCorK5t03IMPPsjJJ5/M1ltvzU477USfPn147rnn2HfffTOK1Gx9TX2f7GZJ0mmS\nnpM0R9JtklpLOkvSK5KeI/koyOq+YyV9K+95Vd729yXNk/SipGvTtm9Lmpm23StpG0lfBb4B/Cw9\n587540r6uqTZ6VhjJG2dtldKukrSC+m+r9STT539JI2SdGlev5ck9Uoff09jeEXSPZIOljRD0quS\n9mrWH7jZZuqXv/wlAwcO5Oyzz+aDDz4AYPHixfTs2bOmT48ePVi8eHGhQrQtVNHOZCX1BU4C9ouI\nVZJ+BZwGXAUMBj4EngBmNzLOESRf5bd3RHwkqUu6676IuCPt81PgnIj4H0kPAX+MiN+n+6rH+Rww\nFvh6RLwi6S7g34Gfp+MtjYhBkv4DuBQ4t56QmtqvWh/gBOBsYCZwCrA/yR8Dl5PMvGvnfB5wHkDX\nrtsxsmx1I6cobt3aJbO9UlXq+VVVVZHL5Wqev/XWW6xYsaKmbeDAgfzmN79BEmPGjOGUU07h+9//\nPosXL+Zvf/tbTb8lS5Ywf/58unbt2vJJNKJ2jqWm1PNrSNEWWeDrJMV0Zlro2gFfBXIR8S6ApInA\nlxsZ52BYBJ5AAAASr0lEQVTgzoj4CCAi3k/bB6TFtTPQAWjsroldgYUR8Ur6fBzJFytUF9n70n9n\nAd9sYJym9qu2MCLmAUiaDzwWESFpHtCrrgMi4nbgdoAde/eJG+YV869B40aUraaUcyz1/MYe3p6K\nioqa55WVlbRvv25btd69e3P00UdTUVHB008/DVDTb/To0Rx66KGb5XJxLperM59SUer5NaSYl4sF\njIuI8vSxKzCqgf6rSfOV1ArYqpHxxwIXRkQZyez4c5sY7yfpv2tI/7iRNDVddv7fhvrlx576XB39\nAdbmPV9Lcf8RZdYkS5Ysqdm+//77a+48/sY3vsGECRP45JNPWLhwIa+++ip77eUrKNayivl/wo8B\nD0q6KSLeSZd5ZwM3S/oCydfynQC8mPavJJn5TiJZSm2btv8ZGCnpnurl4nQ22xFYIqktcCpQfTFn\nebqvtgVAL0l9IuIfwOnAkw0lEBGHNTHXSuBoAEmDgJ2aeFyj2rVtzYJrj2qu4TZLuVyOylMrCh1G\nZraE/KoNHTqUXC7H0qVL6dGjB1dddRW5XI45c+YgiV69enHbbcnXXvfv358TTzyRfv360aZNG265\n5RZat25doCxsS1W0RTYiXpZ0JfBoOjNdRbI8Owp4GlgGzMk75A6SovwiMAVYkY4zRVI58LykT0m+\n/OBy4IfAsyRfjPAsnxXWCcAdkr4D1NxIFREfSzoLmJx+FeBM4NZmSvde4Ix0OfhZ4JVG+puVpPHj\nx6/Xds4559Tb/4orruCKK67IMiSzBhVtkQWIiInAxFrNzwB31tH3bWCfvKbv5+27Fri2Vv9fA7+u\nY5wZQL+8pmF5+x4Ddq/jmF55288DFetnU3+/iFgJHFrXMcCAvGPyY6nM32dmZi2vmK/JmpmZbdZc\nZM3MzDLiImtmZpYRF1kzM7OMuMiamZllxEXWzMwsIy6yZmZmGXGRNTMzy4iLrJmZWUZcZM3MzDLi\nImtmZpYRF1kzM7OMuMiamZllxEXWzMwsIy6yZmZmGXGRNTMzy4iLrJmZWUZcZM1sHWeffTbbb789\nAwYMqGmbPHky/fv3p1WrVjz//PPr9J87dy777rsv/fv3p6ysjI8//rilQzbbbLnItiBJ35H0N0n3\nNNCnqhnOM0zSFzd1HNsyDRs2jClTpqzTNmDAAO677z4OPPDAddpXr17Naaedxq233sr8+fPJ5XK0\nbdu2JcM126y1KXQAW5j/AA6OiDcyPs8w4CXgzcY6rly1hl6XPZxxOIU1omw1w0o4x+bKr/LaowA4\n8MADqaysXGdf37596zzm0UcfZeDAgey2224AfOELX9jkOMxKiWeyLUTSrUBv4E+SPpQ0RlJO0muS\nvlNH/1skfSPdvl/SmHT7bElXp9s/lLRA0lOSxku6VNK3gD2AeyTNkdSu5bK0Lc0rr7yCJA477DAG\nDRrEddddV+iQzDYrnsm2kIgYLulw4CDgQuDQdLsjsEDSryNiVd4h04EDgIeAHYDuafsBwARJewLH\nA7sBbYEXgFkR8XtJFwKXRsS6F89Sks4DzgPo2nU7Rpatbt5kNzPd2iWzvVLVXPnlcrma7bfeeosV\nK1as0wawbNkyZs2aRVVVclVjwYIF/OUvf+HWW29l6623ZsSIEbRu3ZrBgwdvcjzVqqqq1ouj1JR6\njqWeX0NcZAvn4Yj4BPhE0jtANyB/GXk6cLGkfsDLwLaSugP7At8BzgEejIiPgY8l/aGpJ46I24Hb\nAXbs3SdumFfavwYjylZTyjk2V36Vp1Z8tl1ZSfv27amoqFinT+fOnRk8eDB77LEHkBTjjz76iGOP\nPRaAmTNnsnbt2vWO2xS5XK5Zx9sclXqOpZ5fQ7xcXDif5G2vodYfPBGxGOgMHA5MIym6JwJVEbG8\npYI0a8hhhx3GvHnz+Oijj1i9ejVPPvkk/fr1K3RYZpuN0v3zvjQ8A1wMfA34AvD79AEwA7hN0miS\n1/Fo0tkpsJxkGbpR7dq2ZkF6w0upyuVy68zSSk1z5zd06FByuRxLly6lR48eXHXVVXTp0oWLLrqI\nd999l6OOOory8nKmTp3KtttuyyWXXMKee+6JJI488kiOOqq0f5/MNoSL7OZtOnBoRPxD0utAl7SN\niJgp6SFgLvA2MA/4MD1uLHCrpJXAvhGxssUjt6I1fvz4OtuHDBlSZ/tpp53GaaedlmVIZkXLRbYF\nRUSvdHNUrfYBedsd8rZ/A/wm3V4FtK815PURMUrSNiRLyrPSvvcC9zZz+GZmtoFcZIvb7emNUZ8D\nxkXEC4UOyMzMPuMiW8Qi4pRCx2BmZvXz3cVmZmYZcZE1MzPLiIusmZlZRlxkzczMMuIia2ZmlhEX\nWTMzs4y4yJqZmWXERdbMzCwjLrJmZmYZcZE1MzPLiIusmZlZRlxkzczMMuIia2ZmlhEXWTMzs4y4\nyJqZmWXE3ydrlqdXr1507NiR1q1b06ZNG55//nlOOukkFixYAMCyZcvo3Lkzc+bMKXCkZlYMXGTN\nanniiSfo2rVrzfOJEyfWbI8YMYJOnToVIiwzK0ItWmQlBXBPRJyWPm8DLAGejYijN3Hsv0bEVzeg\n/yigKiKub2L/4cBHEXHXRoa4WVq5ag29Lnu40GFkakTZaoY1kGPltUc1aZyIYNKkSTz++OPNFZqZ\nlbiWvia7AhggqV36/BBg8YYMkBbm9Z5vSIHdGBFxa6kVWFufJA499FAGDx7M7bffvs6+6dOn061b\nN3bZZZcCRWdmxaYQNz49AlRPHYYC46t3SNpL0tOSZkv6q6Rd0/Zhkh6S9DjwmKQKSdMlPQS8nPap\nyhvne5JmSpor6aq89iskvSLpKWDX+gKUdEZ67IuS7k7bRkm6VNJXJD2X17eXpHl1jNFd0jRJcyS9\nJOmA6jgl3SRpvqTHJG2XtpdLeiY97/2Stk3bc5L2SLe7SqpMt/tLei4df66kXdL20/Lab5PUusmv\njPHUU0/xwgsv8Kc//YlbbrmFadOm1ewbP348Q4cOLWB0ZlZsCnFNdgIwUtIfgYHAGOCAdN/fgQMi\nYrWkg4FrgOPTfYOAgRHxvqSK9PmAiFiYP7ikQ4FdgL0AAQ9JOpBkFn0yUE6S9wvArNrBSeoPXAl8\nNSKWSuqSvz8i/i5pK0k7pec+CZhYexzgFGBqRFydFrpt0vb2wPMR8Z+SRgI/Ai4E7gIuiognJf04\nbb+4gZ/jcODmiLhH0lZAa0l903j2i4hVkn4FnJqOnZ/jecB5AF27bsfIstUNnKb4dWuXLBnXJ5fL\nrfP81VdfBWD33Xdn/PjxrF27ljVr1jBx4kRuu+229foXWlVV1WYXU3Mq9fyg9HMs9fwa0uJFNiLm\nSupFMot9pNbuTsC4dFYWQNu8fX+OiPfznj9Xu8CmDk0fs9PnHUiKbkfg/oj4CCCdBdfla8DkiFia\nxvt+HX0mkRSza9N/T6qjz0xgjKS2wAMRUX076lo+K8q/Be6T1AnoHBFPpu3jgMn1xFftaeAKST2A\n+yLiVUlfBwYDMyUBtAPeqX1gRNwO3A6wY+8+ccO80r7/bUTZahrKsfLUCgBWrFjB2rVr6dixIytW\nrODyyy9n5MiRVFRUMGXKFMrKyjjhhBNaKOqmy+VyVFRUFDqMzJR6flD6OZZ6fg0p1P9dHwKuByqA\nL+S1/wR4IiKGpIU4l7dvRa0xaj+vJmB0RNy2TqNU56xQUk/gD+nTWxsPHUiK5GRJ9wGRFri9gepz\njoyI6hn0UcBYSTfWc003GjnXaj5b1v9czUERv5P0bDr+I5LOJ8l9XET8oIl50K5taxY08cafYpXL\n5WoKaUPefvtthgwZAsDq1as55ZRTOPzwwwGYMGGCl4rNbIMVqsiOAZZFxLx06bdaJz67EWrYRo49\nFfiJpHsiokrSDsAqYBpJsRtNkvcxwG0RsYhkCRmoWS6+Py2K70nqUns2GxH/lLQG+CHprDQinq01\nzpeANyLiDklbkyxv30VSML9Fsmx+CvBURHwo6QNJB0TEdOB0oHpWW0kyO30uPa56/N7AaxHxC0k7\nkiy9Pwo8KOmmiHgnXeruGBGvb+TPcovSu3dvXnzxxTr3jR07tmWDMbOSUJAiGxFvAL+oY9d1JMvF\nVwIb9b6SiHg0vTb5dLpkWgWcFhEvSJoIvEiyhDqznuPnS7oaeDItpLOpu+BPBH4G7FRPKBXA9ySt\nSmM4I21fAeyV5vgOny01nwncKmkb4DXgrLT9emBSeh01/2dyInB6Ov5bwDXp9eorgUcltSL54+IC\nwEXWzKwAFNHYaqU1J0lVEdGh0HFU23XXXaP604xKValfD3J+xa/UcyzF/CTNiog9Guvnzy42MzPL\nSCbLxZK+ADxWx66vR8R7WZyzWGxOs1gzM8tWJkU2LaTljXY0MzMrYV4uNjMzy4iLrJmZWUZcZM3M\nzDLiImtmZpYRF1kzM7OMuMiamZllxEXWzMwsIy6yZmZmGXGRNTMzy4iLrJmZWUZcZM3MzDLiImtm\nZpYRF1kzM7OMuMiamZllxEXWzMwsIy6yZmZmGXGRNTMzy4iLrJmZWUYUEYWOwQpI0nJgQaHjyFhX\nYGmhg8iQ8yt+pZ5jKeb3pYjYrrFObVoiEtusLYiIPQodRJYkPV/KOTq/4lfqOZZ6fg3xcrGZmVlG\nXGTNzMwy4iJrtxc6gBZQ6jk6v+JX6jmWen718o1PZmZmGfFM1szMLCMusmZmZhlxkd2CSTpc0gJJ\n/5B0WaHjaQ6SKiXNkzRH0vNpWxdJf5b0avrvtoWOc0NIGiPpHUkv5bXVmZMSv0hf07mSBhUu8qap\nJ79Rkhanr+McSUfm7ftBmt8CSYcVJuqmk9RT0hOSXpY0X9J30/aSeA0byK9kXsNN4SK7hZLUGrgF\nOALoBwyV1K+wUTWbgyKiPO99eZcBj0XELsBj6fNiMhY4vFZbfTkdAeySPs4Dft1CMW6KsayfH8BN\n6etYHhGPAKS/oycD/dNjfpX+Lm/OVgMjIqIfsA9wQZpHqbyG9eUHpfMabjQX2S3XXsA/IuK1iPgU\nmAAcW+CYsnIsMC7dHgccV8BYNlhETAPer9VcX07HAndF4hmgs6TuLRPpxqknv/ocC0yIiE8iYiHw\nD5Lf5c1WRCyJiBfS7eXA34AdKJHXsIH86lN0r+GmcJHdcu0ALMp7/gYN/4dRLAJ4VNIsSeelbd0i\nYkm6/RbQrTChNav6ciql1/XCdLl0TN4Sf1HnJ6kXsDvwLCX4GtbKD0rwNdxQLrJWavaPiEEkS24X\nSDowf2ck71krqfetlWJOJEukOwPlwBLghsKGs+kkdQDuBS6OiH/l7yuF17CO/EruNdwYLrJbrsVA\nz7znPdK2ohYRi9N/3wHuJ1mGert6uS39953CRdhs6supJF7XiHg7ItZExFrgDj5bTizK/CS1JSlA\n90TEfWlzybyGdeVXaq/hxnKR3XLNBHaRtJOkrUhuRHiowDFtEkntJXWs3gYOBV4iyevMtNuZwIOF\nibBZ1ZfTQ8AZ6R2q+wAf5i1JFo1a1yCHkLyOkOR3sqStJe1EcnPQcy0d34aQJOA3wN8i4sa8XSXx\nGtaXXym9hpvC38KzhYqI1ZIuBKYCrYExETG/wGFtqm7A/cl/87QBfhcRUyTNBCZJOgd4HTixgDFu\nMEnjgQqgq6Q3gB8B11J3To8AR5LcTPIRcFaLB7yB6smvQlI5yRJqJXA+QETMlzQJeJnkrtYLImJN\nIeLeAPsBpwPzJM1J2y6ndF7D+vIbWkKv4UbzxyqamZllxMvFZmZmGXGRNTMzy4iLrJmZWUZcZM3M\nzDLiImtmZpYRv4XHzDIhaQ0wL6/puIioLFA4ZgXht/CYWSYkVUVEhxY8X5uIWN1S5zNrCi8Xm1lB\nSOouaVr6XaMvSTogbT9c0guSXpT0WNrWRdID6YfNPyNpYNo+StLdkmYAd0tqLelnkmamfc8vYIpm\nXi42s8y0y/sEoIURMaTW/lOAqRFxdfp9ottI2o7kc24PjIiFkrqkfa8CZkfEcZK+BtxF8sHzkHwf\n8v4RsTL95qUPI2JPSVsDMyQ9mn6lmlmLc5E1s6ysjIjyBvbPBMakHy7/QETMkVQBTKsuihFR/T2z\n+wPHp22PS/qCpM+n+x6KiJXp9qHAQEnfSp93IvlsXBdZKwgXWTMriIiYln4V4VHAWEk3Ah9sxFAr\n8rYFXBQRU5sjRrNN5WuyZlYQkr4EvB0RdwD/CwwCngEOTL+dhbzl4unAqWlbBbC09neypqYC/57O\njpH05fQbmcwKwjNZMyuUCuB7klYBVcAZEfFuel31PkmtSL5j9RBgFMnS8lySb6Y5s+4h+V+gF/BC\n+hVs7wLHZZmEWUP8Fh4zM7OMeLnYzMwsIy6yZmZmGXGRNTMzy4iLrJmZWUZcZM3MzDLiImtmZpYR\nF1kzM7OM/H8nKdk6UUrWyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9b2908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "lgb4 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.1, \n",
    " n_estimators=100, \n",
    " max_bin=50,\n",
    " subsample_for_bin=5000,\n",
    " min_split_gain=0, \n",
    " min_child_weight=3, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=0.6, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model4 = lgb_model_fit(lgb4, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.622074\n",
    "\n",
    "ypred = lgb_model4.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.87143003378\n",
      "AUC:  0.787238401679\n",
      "Precision:  0.948728300363\n",
      "Specificity:  0.793383947939\n",
      "Recall:  0.889589905363\n"
     ]
    }
   ],
   "source": [
    "ypred4=lgb_model4.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred4)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8782\n",
      "AUC Score (Train): 0.936438\n",
      "0:00:00.886732\n",
      "[1 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ak0iKLySj1FER8Vj+RpKOBK6JiN/XaO8KfEhSbA8Gai2y+Vq1bM6Ca4fW162o5HK56l8a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\nM8vI/wdkYsvheUzvtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4d4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "lgb4 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.1, \n",
    " n_estimators=100, \n",
    " max_bin=50,\n",
    " subsample_for_bin=5000,\n",
    " min_split_gain=0, \n",
    " min_child_weight=2, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=0.6, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model4 = lgb_model_fit(lgb4, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.886732\n",
    "\n",
    "ypred = lgb_model4.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.871122939912\n",
      "AUC:  0.787036544593\n",
      "Precision:  0.948324586193\n",
      "Specificity:  0.792095289659\n",
      "Recall:  0.889548093916\n"
     ]
    }
   ],
   "source": [
    "ypred4=lgb_model4.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred4)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)\n",
    "\n",
    "# overfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.91058, std: 0.00572, params: {'learning_rate': 0.01},\n",
       "  mean: 0.92155, std: 0.00528, params: {'learning_rate': 0.05},\n",
       "  mean: 0.92691, std: 0.00472, params: {'learning_rate': 0.1},\n",
       "  mean: 0.92854, std: 0.00452, params: {'learning_rate': 0.2}],\n",
       " {'learning_rate': 0.2},\n",
       " 0.9285375090265123)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'learning_rate':[0.01, 0.05, 0.1, 0.2]\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=50,\n",
    " subsample_for_bin=5000, min_split_gain=0, min_child_weight=3, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=0.6, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model5 = gsearch.fit(features_train, label_train)\n",
    "lgb_model5.grid_scores_, lgb_model5.best_params_, lgb_model5.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.92854, std: 0.00452, params: {'learning_rate': 0.2},\n",
       "  mean: 0.92729, std: 0.00488, params: {'learning_rate': 0.3},\n",
       "  mean: 0.92626, std: 0.00466, params: {'learning_rate': 0.4},\n",
       "  mean: 0.92397, std: 0.00423, params: {'learning_rate': 0.5}],\n",
       " {'learning_rate': 0.2},\n",
       " 0.9285375090265123)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_set = {\n",
    " 'learning_rate':[0.2, 0.3, 0.4, 0.5]\n",
    "}    \n",
    "    \n",
    "gsearch = GridSearchCV(estimator = LGBMClassifier(boosting_type='gbdt', num_leaves=30, \n",
    " max_depth=5, learning_rate=0.1, n_estimators=100, max_bin=50,\n",
    " subsample_for_bin=5000, min_split_gain=0, min_child_weight=3, \n",
    " min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=0.6, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7), \n",
    " param_grid = param_set, scoring='roc_auc',n_jobs=1,iid=False, cv=10)\n",
    "\n",
    "lgb_model5 = gsearch.fit(features_train, label_train)\n",
    "lgb_model5.grid_scores_, lgb_model5.best_params_, lgb_model5.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8854\n",
      "AUC Score (Train): 0.943116\n",
      "0:00:00.843361\n",
      "[1 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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AfaSfwWtmZmaNa/buYknbRMS/IuJDYFL6MDMzs1ZoaSZbfwexpDszjsXMzKyk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\nmWXERdbMzCwjLrJmZmYZcZE1MzPLyP8HkoZdn+s6y+cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e3f4eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=datetime.now()\n",
    "\n",
    "lgb5 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.2, \n",
    " n_estimators=100, \n",
    " max_bin=50,\n",
    " subsample_for_bin=5000,\n",
    " min_split_gain=0, \n",
    " min_child_weight=3, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=0.6, \n",
    " reg_alpha=1, reg_lambda=0, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model5 = lgb_model_fit(lgb5, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.843361\n",
    "\n",
    "ypred = lgb_model5.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.87143003378\n",
      "AUC:  0.79456701684\n",
      "Precision:  0.941999730857\n",
      "Specificity:  0.77829218107\n",
      "Recall:  0.894568690096\n"
     ]
    }
   ],
   "source": [
    "ypred5=lgb_model5.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred5)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8874\n",
      "AUC Score (Train): 0.943892\n",
      "0:00:00.764053\n",
      "[1 0 0 ..., 0 1 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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KTUNFNurZNjMzswY0VGQPkPRvSSuA0nT735JWSPp3UwRoZralRowYwe67706f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe14198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try L2 regularization\n",
    "\n",
    "start=datetime.now()\n",
    "\n",
    "lgb6 = LGBMClassifier(\n",
    " boosting_type='gbdt', \n",
    " num_leaves=30, \n",
    " max_depth=5, \n",
    " learning_rate=0.2, \n",
    " n_estimators=100, \n",
    " max_bin=50,\n",
    " subsample_for_bin=5000,\n",
    " min_split_gain=0, \n",
    " min_child_weight=3, \n",
    " min_child_samples=10, \n",
    " subsample=1, subsample_freq=1, colsample_bytree=0.6, \n",
    " reg_alpha=0, reg_lambda=1, seed=410, nthread=7\n",
    ")\n",
    "\n",
    "lgb_model6 = lgb_model_fit(lgb6, features_train, label_train)\n",
    "\n",
    "stop=datetime.now()\n",
    "execution_time_lgbm = stop-start\n",
    "print(execution_time_lgbm)        # 0:00:00.764053\n",
    "\n",
    "ypred = lgb_model6.predict(features_test) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.87143003378\n",
      "AUC:  0.79412729993\n",
      "Precision:  0.942403445028\n",
      "Specificity:  0.77915376677\n",
      "Recall:  0.894266377219\n"
     ]
    }
   ],
   "source": [
    "ypred6=lgb_model6.predict(features_test)\n",
    "\n",
    "cm = confusion_matrix(label_test, ypred6)\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "accuracy_lgb = (TP + TN)/(TP+FP+FN+TN)   \n",
    "auc_score_lgb = roc_auc_score(label_test, ypred)   \n",
    "precision_lgb = TP/(TP+FP)   \n",
    "specificity_lgb = TN/(TN+FP)  \n",
    "recall_lgb = TP/(TP+FN)   \n",
    "print(\"accuracy: \", accuracy_lgb)\n",
    "print(\"AUC: \", auc_score_lgb)\n",
    "print(\"Precision: \", precision_lgb)\n",
    "print(\"Specificity: \", specificity_lgb)\n",
    "print(\"Recall: \", recall_lgb)\n",
    "\n",
    "# L1 works better in this case"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}