sklearn sklearn中的模型调参利器 gridSearchCV（网格搜索）

代码：

import pandas as pd # 数据科学计算工具
import numpy as np # 数值计算工具
import matplotlib.pyplot as plt # 可视化
import seaborn as sns # matplotlib的高级API
from sklearn.model_selection import StratifiedKFold #交叉验证
from sklearn.model_selection import GridSearchCV #网格搜索
from sklearn.model_selection import train_test_split #将数据集分开成训练集和测试集
from xgboost import XGBClassifier                     #xgboost
 
 
pima = pd.read_csv("pima_indians-diabetes.csv")
print(pima.head())
 
x = pima.iloc[:,0:8]
y = pima.iloc[:,8]
 
seed = 7 #重现随机生成的训练
test_size = 0.33 #33%测试，67%训练
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=test_size, random_state=seed
model = XGBClassifier()               
learning_rate = [0.0001,0.001,0.01,0.1,0.2,0.3] #学习率
gamma = [1, 0.1, 0.01, 0.001]
 
param_grid = dict(learning_rate = learning_rate,gamma = gamma)#转化为字典格式，网络搜索要求
 
kflod = StratifiedKFold(n_splits=10, shuffle = True,random_state=7)#将训练/测试数据集划分10个互斥子集，
 
grid_search = GridSearchCV(model,param_grid,scoring = 'neg_log_loss',n_jobs = -1,cv = kflod)
#scoring指定损失函数类型，n_jobs指定全部cpu跑，cv指定交叉验证
grid_result = grid_search.fit(X_train, Y_train) #运行网格搜索
print("Best: %f using %s" % (grid_result.best_score_,grid_search.best_params_))
#grid_scores_：给出不同参数情况下的评价结果。best_params_：描述了已取得最佳结果的参数的组合
#best_score_：成员提供优化过程期间观察到的最好的评分
#具有键作为列标题和值作为列的dict，可以导入到DataFrame中。
#注意，“params”键用于存储所有参数候选项的参数设置列表。
means = grid_result.cv_results_['mean_test_score']
params = grid_result.cv_results_['params']
for mean,param in zip(means,params):
    print("%f with: %r" % (mean,param))