什么是Grid Search 网格搜索?

Grid Search:一种调参手段;穷举搜索:在所有候选的参数选择中,通过循环遍历,尝试每一种可能性,表现最好的参数就是最终的结果。其原理就像是在数组里找最大值。(为什么叫网格搜索?以有两个参数的模型为例,参数a有3种可能,参数b有4种可能,把所有可能性列出来,可以表示成一个3*4的表格,其中每个cell就是一个网格,循环过程就像是在每个网格里遍历、搜索,所以叫grid search)
调参必备---GridSearch网格搜索 人工智能 第1张

Simple Grid Search:简单的网格搜索

以2个参数的调优过程为例:

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from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

iris = load_iris()
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=0)
print("Size of training set:{} size of testing set:{}".format(X_train.shape[0],X_test.shape[0]))

####   grid search start
best_score = 0
for gamma in [0.001,0.01,0.1,1,10,100]:
    for C in [0.001,0.01,0.1,1,10,100]:
        svm = SVC(gamma=gamma,C=C)#对于每种参数可能的组合,进行一次训练;
        svm.fit(X_train,y_train)
        score = svm.score(X_test,y_test)
        if score > best_score:#找到表现最好的参数
            best_score = score
            best_parameters = {'gamma':gamma,'C':C}
####   grid search end

print("Best score:{:.2f}".format(best_score))
print("Best parameters:{}".format(best_parameters))

输出:

Size of training set:112 size of testing set:38
Best score:0.973684
Best parameters:{'gamma': 0.001, 'C': 100}

存在的问题:

原始数据集划分成训练集和测试集以后,其中测试集除了用作调整参数,也用来测量模型的好坏;这样做导致最终的评分结果比实际效果要好。(因为测试集在调参过程中,送到了模型里,而我们的目的是将训练模型应用在unseen data上);

解决方法:

对训练集再进行一次划分,分成训练集和验证集,这样划分的结果就是:原始数据划分为3份,分别为:训练集、验证集和测试集;其中训练集用来模型训练,验证集用来调整参数,而测试集用来衡量模型表现好坏。
调参必备---GridSearch网格搜索 人工智能 第2张

X_trainval,X_test,y_trainval,y_test = train_test_split(iris.data,iris.target,random_state=0)
X_train,X_val,y_train,y_val = train_test_split(X_trainval,y_trainval,random_state=1)
print("Size of training set:{} size of validation set:{} size of teseting set:{}".format(X_train.shape[0],X_val.shape[0],X_test.shape[0]))

best_score = 0.0
for gamma in [0.001,0.01,0.1,1,10,100]:
    for C in [0.001,0.01,0.1,1,10,100]:
        svm = SVC(gamma=gamma,C=C)
        svm.fit(X_train,y_train)
        score = svm.score(X_val,y_val)
        if score > best_score:
            best_score = score
            best_parameters = {'gamma':gamma,'C':C}
svm = SVC(**best_parameters) #使用最佳参数,构建新的模型
svm.fit(X_trainval,y_trainval) #使用训练集和验证集进行训练,more data always results in good performance.
test_score = svm.score(X_test,y_test) # evaluation模型评估
print("Best score on validation set:{:.2f}".format(best_score))
print("Best parameters:{}".format(best_parameters))
print("Best score on test set:{:.2f}".format(test_score))

输出:

Size of training set:84 size of validation set:28 size of teseting set:38
Best score on validation set:0.96
Best parameters:{'gamma': 0.001, 'C': 10}
Best score on test set:0.92
然而,这种间的的grid search方法,其最终的表现好坏与初始数据的划分结果有很大的关系,为了处理这种情况,我们采用交叉验证的方式来减少偶然性。

Grid Search with Cross Validation

from sklearn.model_selection import cross_val_score

best_score = 0.0
for gamma in [0.001,0.01,0.1,1,10,100]:
    for C in [0.001,0.01,0.1,1,10,100]:
        svm = SVC(gamma=gamma,C=C)
        scores = cross_val_score(svm,X_trainval,y_trainval,cv=5) #5折交叉验证
        score = scores.mean() #取平均数
        if score > best_score:
            best_score = score
            best_parameters = {"gamma":gamma,"C":C}
svm = SVC(**best_parameters)
svm.fit(X_trainval,y_trainval)
test_score = svm.score(X_test,y_test)
print("Best score on validation set:{:.2f}".format(best_score))
print("Best parameters:{}".format(best_parameters))
print("Score on testing set:{:.2f}".format(test_score))

输出:

Best score on validation set:0.97
Best parameters:{'gamma': 0.01, 'C': 100}
Score on testing set:0.97

交叉验证经常与网格搜索进行结合,作为参数评价的一种方法,这种方法叫做grid search with cross validation。sklearn因此设计了一个这样的类GridSearchCV,这个类实现了fit,predict,score等方法,被当做了一个estimator,使用fit方法,该过程中:(1)搜索到最佳参数;(2)实例化了一个最佳参数的estimator;

from sklearn.model_selection import GridSearchCV

#把要调整的参数以及其候选值 列出来;
param_grid = {"gamma":[0.001,0.01,0.1,1,10,100],
             "C":[0.001,0.01,0.1,1,10,100]}
print("Parameters:{}".format(param_grid))

grid_search = GridSearchCV(SVC(),param_grid,cv=5) #实例化一个GridSearchCV类
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=10)
grid_search.fit(X_train,y_train) #训练,找到最优的参数,同时使用最优的参数实例化一个新的SVC estimator。
print("Test set score:{:.2f}".format(grid_search.score(X_test,y_test)))
print("Best parameters:{}".format(grid_search.best_params_))
print("Best score on train set:{:.2f}".format(grid_search.best_score_))

输出:

Parameters:{'gamma': [0.001, 0.01, 0.1, 1, 10, 100], 'C': [0.001, 0.01, 0.1, 1, 10, 100]}
Test set score:0.97
Best parameters:{'C': 10, 'gamma': 0.1}
Best score on train set:0.98
Grid Search 调参方法存在的共性弊端就是:耗时;参数越多,候选值越多,耗费时间越长!所以,一般情况下,先定一个大范围,然后再细化。

调参必备---GridSearch网格搜索 人工智能 第3张

总而言之,言而总之

  • Grid Search:一种调优方法,在参数列表中进行穷举搜索,对每种情况进行训练,找到最优的参数;由此可知,这种方法的主要缺点是 比较耗时!
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