model=XGBClassifier(learning_rate=0.96,
n_estimators=91,
max_depth=2,
min_child_weight=1,
objective='binary:logistic',
subsample=0.9,
feature_fraction=0.5,
# reg_alpha=8,
# reg_lambda=12,
# colsample_bytree=0.99,
# nthread=8,
# scale_pos_weight=1,
seed=1330)
def rf_cv(learning_rate,n_estimators, max_depth, min_child_weight,subsample,reg_alpha,reg_lambda):
val = cross_val_score(
XGBClassifier(
learning_rate=learning_rate,
n_estimators=int(n_estimators),
max_depth=int(max_depth),
min_child_weight=min_child_weight,
subsample=min(subsample, 0.999),
# num_leaves=int(num_leaves),
reg_alpha=int(reg_alpha),
reg_lambda=int(reg_lambda)
# feature_fraction=min(feature_fraction, 0.999),
# random_state=int(random_state)
),
train_x, train_y, scoring='roc_auc', cv=5
).mean()
return val
params= {
'learning_rate': (0.1, 1.5),
'n_estimators': (1, 100),
'max_depth': (1, 100),
'min_child_weight': (0.1, 0.999),
'subsample': (0.1, 0.999),
#'num_leaves':(1,200),
# 'feature_fraction': (0.1, 0.999),
#'random_state': (2, 2500),
'reg_alpha':(1,9),
'reg_lambda':(5,20),
}
#实例化贝叶斯优化函数
rf_bo = BayesianOptimization(rf_cv, params)
#最大化目标函数，贝叶斯中只能最大化目标函数，因此当需要求最小化目标函数时，需要将目标函数取相反数。
rf_bo.maximize(init_points=5,n_iter=50)
params=rf_bo.max
params=params["params"]
params#打印最优参数
val=rf_cv(**params)
val#打印交叉准确率
