封装GridSearchCV的训练包

import xgboost as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import ParameterGrid
from sklearn.model_selection import ParameterSampler
from scipy.stats.distributions import expon
import numpy as np

#从给定分布中生成采样参数
np.random.seed(0)
param = {"a":[1,2],"b":expon()}
param = list(ParameterSampler(param,n_iter=4))

##################################################################################

def get_model_GridSearchCV(estimator,parameters,X_train,y_train,scoring,cv=5):
    """
    return:返回训练过的最好模型
    """

    #refit:Refit an estimator using the best found parameters on the whole dataset.
    model = GridSearchCV(estimator=estimator,param_grid=parameters,scoring=scoring,cv=5,refit=True)    

    model.fit(X_train, y_train)

    #打印结果
    print("best score in GridSearchCV:\n",model.best_score_)
    print("best param in GridSearchCV:\n",model.best_params_)     

    return model.best_estimator_

#########################################测试########################################

X,y = load_breast_cancer(return_X_y=True)  

#分隔训练集和测试集
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0,stratify=y)

#配置参数
param = [{
         "learning_rate":[0.1,0.3,0.6],
         "max_depth":[5,6,7],
         "n_estimators":[100,200,300],
        }]

#scoring = make_scorer(accuracy_score, greater_is_better=True)

estimator = xgb.XGBClassifier(objective="reg:logistic")

#训练模型
model = get_model_GridSearchCV(estimator=estimator,
                               parameters=param,
                               cv=5,
                               X_train=X_train,
                               y_train=y_train,
                               scoring="roc_auc")

#采用训练得模型做测试
"""
decision_function(*args, **kwargs)    Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups])    Run fit with all sets of parameters.
get_params([deep])    Get parameters for this estimator.
inverse_transform(*args, **kwargs)    Call inverse_transform on the estimator with the best found params.
predict(*args, **kwargs)    Call predict on the estimator with the best found parameters.
predict_log_proba(*args, **kwargs)    Call predict_log_proba on the estimator with the best found parameters.
predict_proba(*args, **kwargs)    Call predict_proba on the estimator with the best found parameters.
score(X[, y])    Returns the score on the given data, if the estimator has been refit.
set_params(**params)    Set the parameters of this estimator.
transform(*args, **kwargs)    Call transform on the estimator with the best found parameters.
"""
y_pred = model.predict(X_test)

#模型评价
print(accuracy_score(y_test,y_pred))