调参必备---GridSearch网格搜索
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)
Simple Grid Search:简单的网格搜索
以2个参数的调优过程为例:
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。
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。
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!