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('\nFrequency 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('\nStatistical 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("\nModel 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))

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