XGBOOST应用及调参示例

该示例所用的数据可从该链接下载，提取码为3y90，数据说明可参考该网页。该示例的“模型调参”这一部分引用了这篇博客的步骤。

数据前处理

1. 导入数据

   import pandas as pd
   import numpy as np
   from sklearn.cross_validation import train_test_split
   ### Load data
   ### Split the data to train and test sets
   data = pd.read_csv('data/loan/Train.csv', encoding = "ISO-8859-1")
   train, test = train_test_split(data,train_size=0.7,random_state=123,stratify=data['Disbursed'])
   ### Check number of nulls in each feature column
   nulls_per_column = train.isnull().sum()
   print(nulls_per_column)

2. 将特征拆分成数值型和种类型

   ### Drop the useless columns
   train_1 = train.drop(['ID','Lead_Creation_Date','LoggedIn'],axis=1)
   ### Split the columns to numerical and categorical
   category_cols = train_1.columns[train_1.dtypes==object].tolist()
   category_cols.remove('DOB')
   category_cols.append('Var4')
   numeric_cols = list(set(train_1.columns)-set(category_cols))

3. 分析并处理种类型特征

   ### explore the categorical columns
   for v in category_cols:
       print('Ratio of missing value for variable {0}: {1}'.format(v,nulls_per_column[v]/train_1.shape[0]))
   print('-----------------------------------------------------------')
   counts = dict()
   for v in category_cols:
       print('
   Frequency count for variable %s'%v)
       counts[v] = train_1[v].value_counts()
       print(counts[v])
   ### merge the cities that counts<200
   merge_city = [c for c in counts['City'].index if counts['City'][c]<200]
   train_1['City'] = train_1['City'].apply(lambda x: 'others' if x in merge_city else x)
   ### merge the salary accounts that counts<100
   merge_sa = [c for c in counts['Salary_Account'].index if counts['Salary_Account'][c]<100]
   train_1['Salary_Account'] = train_1['Salary_Account'].apply(lambda x: 'others' if x in merge_sa else x)
   ### merge the sources that counts<100
   merge_sr = [c for c in counts['Source'].index if counts['Source'][c]<100]
   train_1['Source'] = train_1['Source'].apply(lambda x: 'others' if x in merge_sr else x)
   ### impute the missing value
   train_1['City'].fillna('Missing',inplace=True)
   train_1['Salary_Account'].fillna('Missing',inplace=True)
   ### delete the column Employer_Name since too many categories
   train_2 = train_1.drop('Employer_Name',axis=1)

4. 分析并处理数值型特征

   ### Explore the numerical columns
   for v in numeric_cols:
       print('Ratio of missing value for variable {0}: {1}'.format(v,nulls_per_column[v]/train_2.shape[0]))
   print('-----------------------------------------------------------')
   for v in numeric_cols:
       print('
   Statistical summary for variable %s'%v)
       print(train_2[v].describe())
   ### Create Age column:
   train_2['Age'] = train_2['DOB'].apply(lambda x: 118 - int(x[-2:]))
   ### High proportion missing so create a new variable stating whether this is missing or not:
   train_2['Loan_Amount_Submitted_Missing'] = train_2['Loan_Amount_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)
   train_2['Loan_Tenure_Submitted_Missing'] = train_2['Loan_Tenure_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)
   train_2['EMI_Loan_Submitted_Missing'] = train_2['EMI_Loan_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)
   train_2['Interest_Rate_Missing'] = train_2['Interest_Rate'].apply(lambda x: 1 if pd.isnull(x) else 0)
   train_2['Processing_Fee_Missing'] = train_2['Processing_Fee'].apply(lambda x: 1 if pd.isnull(x) else 0)
   ### Impute the missing value
   train_2['Existing_EMI'].fillna(train_2['Existing_EMI'].median(), inplace=True)
   train_2['Loan_Amount_Applied'].fillna(train_2['Loan_Amount_Applied'].median(),inplace=True)
   train_2['Loan_Tenure_Applied'].fillna(train_2['Loan_Tenure_Applied'].median(),inplace=True)
   ### Drop original columns
   train_3 = train_2.drop(['DOB','Loan_Amount_Submitted','Loan_Tenure_Submitted','EMI_Loan_Submitted', 
                           'Interest_Rate','Processing_Fee'],axis=1)

5.  One-Hot encoding

   from sklearn.preprocessing import LabelEncoder
   dropped_columns = ['ID','Lead_Creation_Date','LoggedIn','Employer_Name','DOB','Loan_Amount_Submitted', 
                   'Loan_Tenure_Submitted','EMI_Loan_Submitted','Interest_Rate','Processing_Fee']
   le = LabelEncoder()
   var_to_encode = list(set(category_cols)-set(dropped_columns))
   for col in var_to_encode:
       train_3[col] = le.fit_transform(train_3[col])
   ### pd.get_dummies can also be used directly without LabelEncoder    
   train_3 = pd.get_dummies(train_3, columns=var_to_encode)

模型调参

1. 建立基础模型并使用early_stop调整迭代次数

   import xgboost as xgb
   import matplotlib.pyplot as plt
   from sklearn import metrics
   ### base model      
   target = 'Disbursed'
   predictors = [x for x in train_3.columns if x!=target]
   xgb1 = xgb.XGBClassifier(learning_rate=0.1, n_estimators=1000, max_depth=5, min_child_weight=1, gamma=0, 
                            subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   ### use early_stop in xgb.cv
   def get_n_estimators(alg, dtrain, predictors, target, cv_folds=5, early_stopping_rounds=50):
       xgb_param = alg.get_xgb_params()
       xgtrain = xgb.DMatrix(dtrain[predictors], label=dtrain[target])
       cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds, 
                         metrics='auc', early_stopping_rounds=early_stopping_rounds, stratified=True)
       alg.set_params(n_estimators=cvresult.shape[0])   
       #Print model report:
       print("
   Model Report")
       print("Set n_estimators to {0}".format(cvresult.shape[0]))
       print(cvresult.tail(1)['test-auc-mean'])
       #Fit the algorithm on the data
       alg.fit(dtrain[predictors], dtrain[target], eval_metric='auc')
       #Feature importance
       feat_imp = pd.Series(alg.get_booster().get_fscore()).sort_values(ascending=False)
       feat_imp.plot(kind='bar', title='Feature Importances', figsize=(20,6))
       plt.ylabel('Feature Importance Score')
       return
   ### get n_estimators
   get_n_estimators(xgb1, train_3, predictors, target) 

2. Tune max_depth and min_child_weight

   from sklearn.model_selection import GridSearchCV
   ### optimal: {'max_depth':5,'min_child_weight':5}
   param_test1 = {'max_depth':range(3,10,2),'min_child_weight':range(1,6,2)}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=141, max_depth=5, min_child_weight=1, gamma=0, 
                           subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch1 = GridSearchCV(estimator = alg, param_grid = param_test1, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch1.fit(train_3[predictors],train_3[target])
   print(gsearch1.best_params_)
   print(gsearch1.best_score_)
   ### optimal: {'max_depth':4,'min_child_weight':6}
   param_test2 = {'max_depth':[4,5,6],'min_child_weight':[4,5,6]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=141, max_depth=5, min_child_weight=5, gamma=0, 
                           subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch2 = GridSearchCV(estimator = alg, param_grid = param_test2, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch2.fit(train_3[predictors],train_3[target])
   print(gsearch2.best_params_)
   print(gsearch2.best_score_)
   ### optimal: {'min_child_weight':6}
   param_test2b = {'min_child_weight':[6,8,10,12]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=141, max_depth=4, min_child_weight=6, gamma=0, 
                           subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch2b = GridSearchCV(estimator = alg, param_grid = param_test2b, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch2b.fit(train_3[predictors],train_3[target])
   print(gsearch2b.best_params_)
   print(gsearch2b.best_score_)

3. Tune gamma

   ### optimal: {'gamma':0.2}
   param_test3 = {'gamma':[i/10.0 for i in range(0,5)]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=141, max_depth=4, min_child_weight=6, gamma=0, 
                           subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch3 = GridSearchCV(estimator = alg, param_grid = param_test3, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch3.fit(train_3[predictors],train_3[target])
   print(gsearch3.best_params_)
   print(gsearch3.best_score_)
   ### get n_estimators
   xgb2 = xgb.XGBClassifier(learning_rate=0.1, n_estimators=1000, max_depth=4, min_child_weight=6, gamma=0.2, 
                            subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   get_n_estimators(xgb2, train_3, predictors, target)

4. Tune subsample and colsample_bytree

   ### optimal: {'colsample_bytree': 0.7, 'subsample': 0.7}
   param_test4 = {'subsample':[i/10.0 for i in range(6,11)], 'colsample_bytree':[i/10.0 for i in range(6,11)]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=142, max_depth=4, min_child_weight=6, gamma=0.2, 
                           subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch4 = GridSearchCV(estimator = alg, param_grid = param_test4, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch4.fit(train_3[predictors],train_3[target])
   print(gsearch4.best_params_)
   print(gsearch4.best_score_)
   ### optimal: {'colsample_bytree': 0.75, 'subsample': 0.7}
   param_test5 = {'subsample':[i/100.0 for i in range(65,80,5)], 'colsample_bytree':[i/100.0 for i in range(65,80,5)]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=142, max_depth=4, min_child_weight=6, gamma=0.2, 
                           subsample=0.7, colsample_bytree=0.7, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch5 = GridSearchCV(estimator = alg, param_grid = param_test5, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch5.fit(train_3[predictors],train_3[target])
   print(gsearch5.best_params_)
   print(gsearch5.best_score_)

5. Tune reg_alpha

   ### optimal: {'reg_alpha': 0.01}
   param_test6 = {'reg_alpha':[0, 1e-5, 1e-2, 0.1, 1, 100]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=142, max_depth=4, min_child_weight=6, gamma=0.2, 
                           subsample=0.7, colsample_bytree=0.75, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch6 = GridSearchCV(estimator = alg, param_grid = param_test6, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch6.fit(train_3[predictors],train_3[target])
   print(gsearch6.best_params_)
   print(gsearch6.best_score_)
   ### optimal: {'reg_alpha': 0.01}
   param_test7 = {'reg_alpha':[0.001, 0.005, 0.01, 0.05]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=142, max_depth=4, min_child_weight=6, gamma=0.2, reg_alpha=0.01, 
                           subsample=0.7, colsample_bytree=0.75, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch7 = GridSearchCV(estimator = alg, param_grid = param_test7, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch7.fit(train_3[predictors],train_3[target])
   print(gsearch7.best_params_)
   print(gsearch7.best_score_)

6. Tune reg_lambda

   ### optimal: {'reg_lambda': 1}
   param_test8 = {'reg_lambda':[0, 0.01, 0.1, 1, 10, 100]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=142, max_depth=4, min_child_weight=6, gamma=0.2, reg_alpha=0.01, 
                           subsample=0.7, colsample_bytree=0.75, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch8 = GridSearchCV(estimator = alg, param_grid = param_test8, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch8.fit(train_3[predictors],train_3[target])
   print(gsearch8.best_params_)
   print(gsearch8.best_score_)
   ### optimal: {'reg_lambda': 1}
   param_test9 = {'reg_lambda':[0.5, 0.7, 1, 3, 5]}
   alg = xgb.XGBClassifier(learning_rate=0.1, n_estimators=142, max_depth=4, min_child_weight=6, gamma=0.2, reg_alpha=0.01, 
                           subsample=0.7, colsample_bytree=0.75, objective= 'binary:logistic', nthread=4, seed=27)
   gsearch9 = GridSearchCV(estimator = alg, param_grid = param_test9, scoring='roc_auc', n_jobs=4, iid=False, cv=5)
   gsearch9.fit(train_3[predictors],train_3[target])
   print(gsearch9.best_params_)
   print(gsearch9.best_score_)
   ### get n_estimators
   xgb3 = xgb.XGBClassifier(learning_rate=0.1, n_estimators=1000, max_depth=4, min_child_weight=6, gamma=0.2, 
                            reg_alpha=0.01, reg_lambda=1, subsample=0.7, colsample_bytree=0.75, 
                            objective= 'binary:logistic', nthread=4, seed=27)
   get_n_estimators(xgb3, train_3, predictors, target)

7. Reduce learning rate

   xgb4 = xgb.XGBClassifier(learning_rate=0.01, n_estimators=5000, max_depth=4, min_child_weight=6, gamma=0.2, 
                            reg_alpha=0.01, reg_lambda=1, subsample=0.7, colsample_bytree=0.75, 
                            objective= 'binary:logistic', nthread=4, seed=27)
   get_n_estimators(xgb4, train_3, predictors, target)

根据上述过程构建完整的Pipeline

import pandas as pd
import numpy as np
import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import Imputer, FunctionTransformer, LabelBinarizer
from sklearn_pandas import DataFrameMapper, CategoricalImputer
from sklearn.pipeline import Pipeline

data = pd.read_csv('Train.csv', encoding = "ISO-8859-1")
train, test = train_test_split(data,train_size=0.7,random_state=123,stratify=data['Disbursed'])

target_raw = 'Disbursed'
predictors_raw = [col for col in train.columns if col!=target_raw]
train_X, train_y = train[predictors_raw], train[target_raw]

category_cols = train_X.columns[train_X.dtypes==object].tolist()
category_cols.remove('DOB')
category_cols.append('Var4')
numeric_cols = list(set(train_X.columns)-set(category_cols))
numeric_cols = numeric_cols+['Age', 'Loan_Amount_Submitted_Missing', 'Loan_Tenure_Submitted_Missing', 
                             'EMI_Loan_Submitted_Missing', 'Interest_Rate_Missing', 'Processing_Fee_Missing']

counts = dict()
for v in category_cols:
    counts[v] = train_X[v].value_counts()
non_merge_city = [c for c in counts['City'].index if counts['City'][c]>=200]
non_merge_sa = [c for c in counts['Salary_Account'].index if counts['Salary_Account'][c]>=100]
non_merge_sr = [c for c in counts['Source'].index if counts['Source'][c]>=100]

dropped_columns = ['ID','Lead_Creation_Date','LoggedIn','Employer_Name','DOB','Loan_Amount_Submitted', 
                   'Loan_Tenure_Submitted','EMI_Loan_Submitted','Interest_Rate','Processing_Fee']

# Function Transform
def preprocess(X):
    X['City'] = X['City'].apply(lambda x: 'others' if x not in non_merge_city and not pd.isnull(x) else x)
    X['Salary_Account'] = X['Salary_Account'].apply(lambda x: 'others' if x not in non_merge_sa and not pd.isnull(x) else x)
    X['Source'] = X['Source'].apply(lambda x: 'others' if x not in non_merge_sr and not pd.isnull(x) else x)

    X['Age'] = X['DOB'].apply(lambda x: 118 - int(x[-2:]))

    X['Loan_Amount_Submitted_Missing'] = X['Loan_Amount_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)
    X['Loan_Tenure_Submitted_Missing'] = X['Loan_Tenure_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)
    X['EMI_Loan_Submitted_Missing'] = X['EMI_Loan_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)
    X['Interest_Rate_Missing'] = X['Interest_Rate'].apply(lambda x: 1 if pd.isnull(x) else 0)
    X['Processing_Fee_Missing'] = X['Processing_Fee'].apply(lambda x: 1 if pd.isnull(x) else 0)

    return X.drop(dropped_columns, axis=1)

# Apply numeric imputer
numeric_imputer = [([feature], Imputer(strategy="median")) for feature in numeric_cols if feature not in dropped_columns]
# Apply categorical imputer and one-hot encode
category_imputer = [(feature, [CategoricalImputer(strategy='constant', fill_value='Missing'),LabelBinarizer()]) 
                    for feature in category_cols if feature not in dropped_columns]
# Combine the numeric and categorical transformations
numeric_categorical_union = DataFrameMapper(numeric_imputer+category_imputer,input_df=True,df_out=True)

# Tuned Classifier
tuned_xgb = xgb.XGBClassifier(learning_rate=0.01, n_estimators=1480, max_depth=4, min_child_weight=6, gamma=0.2, 
                              reg_alpha=0.01, reg_lambda=1, subsample=0.7, colsample_bytree=0.75, 
                              objective= 'binary:logistic', nthread=4, seed=27)

# Create full pipeline
pipeline = Pipeline([("preprocessor", FunctionTransformer(preprocess, validate=False)), 
                     ("featureunion", numeric_categorical_union), ("classifier", tuned_xgb)])
pipeline.fit(train_X, train_y)

#Feature importance
feat_imp = pd.Series(pipeline.named_steps['classifier'].get_booster().get_fscore()).sort_values(ascending=False)
feat_imp.plot(kind='bar', title='Feature Importances', figsize=(20,6))
plt.ylabel('Feature Importance Score')

# individual prediction
print(pipeline.predict_proba(test.iloc[[1]][predictors_raw]))
# test data predictions
# AUC Score (Test): 0.8568
predprob=pipeline.predict_proba(test[predictors_raw])[:,1]
print("AUC Score (Test): %f" % metrics.roc_auc_score(test[target_raw], predprob))