用贝叶斯优化的方法优化xgboost的参数_from skopt import bayessearchcv

除了我们平常所做的网格搜索，随机搜索外，我发现贝叶斯优化的方法挺不错，然后我就尝试了一下，发现效果挺好的，我这里把我的代码分享出来：

贝叶斯优化通过基于目标函数的过去评估结果建立替代函数（概率模型），来找到最小化目标函数的值。贝叶斯方法与随机或网格搜索的不同之处在于，它在尝试下一组超参数时，会参考之前的评估结果，因此可以省去很多无用功。

超参数的评估代价很大，因为它要求使用待评估的超参数训练一遍模型，而许多深度学习模型动则几个小时几天才能完成训练，并评估模型，因此耗费巨大。贝叶斯调参发使用不断更新的概率模型，通过推断过去的结果来“集中”有希望的超参数。

1 导入库包

from skopt import BayesSearchCV
import xgboost as xgb
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.model_selection import StratifiedKFold
import numpy as np
from sklearn.utils import shuffle
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2 加载数据

train_path='ads_train.csv'
train_data=pd.read_csv(train_path)
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3 数据集特征处理

train_data = shuffle(train_data)
X=train_data[['isbuyer', 'buy_freq', 'visit_freq', 'buy_interval',
       'sv_interval', 'expected_time_buy', 'expected_time_visit', 'last_buy', 'multiple_buy', 'multiple_visit', 'uniq_urls',
       'num_checkins']]
Y=train_data[['y_buy']]
X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)
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优化代码

ITERATIONS=100
# Classifier
bayes_cv_tuner = BayesSearchCV(
    estimator = xgb.XGBClassifier(
        n_jobs = 1,
        objective = 'binary:logistic',
        eval_metric = 'auc',
        silent=1,
        tree_method='approx'
    ),
    search_spaces = {
        'learning_rate': (0.01, 1.0, 'log-uniform'),
        'min_child_weight': (0, 10),
        'max_depth': (0, 50),
        'max_delta_step': (0, 20),
        'subsample': (0.01, 1.0, 'uniform'),
        'colsample_bytree': (0.01, 1.0, 'uniform'),
        'colsample_bylevel': (0.01, 1.0, 'uniform'),
        'reg_lambda': (1e-9, 1000, 'log-uniform'),
        'reg_alpha': (1e-9, 1.0, 'log-uniform'),
        'gamma': (1e-9, 0.5, 'log-uniform'),
        'min_child_weight': (0, 5),
        'n_estimators': (50, 100),
        'scale_pos_weight': (1e-6, 500, 'log-uniform')
    },    
    scoring = 'roc_auc',
    cv = StratifiedKFold(
        n_splits=5,
        shuffle=True,
        random_state=42
    ),
    n_jobs = 6,
    n_iter = ITERATIONS,   
    verbose = 0,
    refit = True,
    random_state = 42
)

def status_print(optim_result):
    """Status callback durring bayesian hyperparameter search"""
    
    # Get all the models tested so far in DataFrame format
    all_models = pd.DataFrame(bayes_cv_tuner.cv_results_)    
    
    # Get current parameters and the best parameters    
    best_params = pd.Series(bayes_cv_tuner.best_params_)
    print('Model #{}\nBest ROC-AUC: {}\nBest params: {}\n'.format(
        len(all_models),
        np.round(bayes_cv_tuner.best_score_, 4),
        bayes_cv_tuner.best_params_
    ))
    print(dict(bayes_cv_tuner.best_params_))
    
    
    # Save all model results
    clf_name = bayes_cv_tuner.estimator.__class__.__name__
    all_models.to_csv(clf_name+"_cv_results.csv")

result = bayes_cv_tuner.fit(X.values, Y.values, callback=status_print)
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参考文献

Bayesian hyperparameter tuning of xgBoost
自动机器学习超参数调整（贝叶斯优化）