Question

我使用GridSearchCV作为估算工具运行OneVsRestClasssifer SVC。这是我的Pipeline和GridSearchCV参数的一个方面：

pipeline = Pipeline([
    ('clf', OneVsRestClassifier(SVC(verbose=True), n_jobs=1)),
    ])

parameters = {
    "clf__estimator__C": [0.1, 1],
    "clf__estimator__kernel": ['poly', 'rbf'],
    "clf__estimator__degree": [2, 3],
}

grid_search_tune = GridSearchCV(pipeline, parameters, cv=2, n_jobs=8, verbose=10)
grid_search_tune.fit(train_x, train_y)

根据SVC的文档，degree参数仅由poly内核使用：

http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

度：int，可选（默认= 3）

多项式核的度   功能（'poly'）。被所有其他内核忽略。

但是当我看到GridSearchCV的输出时，它似乎计算了具有SVC内核的每个rbf配置的不同运行以及{{{{}的不同值1}}参数。

degree

当内核设置为[CV] clf__estimator__kernel=poly, clf__estimator__C=0.1, clf__estimator__degree=2 [CV] clf__estimator__kernel=poly, clf__estimator__C=0.1, clf__estimator__degree=2 [CV] clf__estimator__kernel=rbf, clf__estimator__C=0.1, clf__estimator__degree=2 [CV] clf__estimator__kernel=rbf, clf__estimator__C=0.1, clf__estimator__degree=2 [CV] clf__estimator__kernel=poly, clf__estimator__C=0.1, clf__estimator__degree=3 [CV] clf__estimator__kernel=poly, clf__estimator__C=0.1, clf__estimator__degree=3 [CV] clf__estimator__kernel=rbf, clf__estimator__C=0.1, clf__estimator__degree=3 [CV] clf__estimator__kernel=rbf, clf__estimator__C=0.1, clf__estimator__degree=3时，是否应该忽略所有度数值？

Answer 1

The output shown here is only the different combinations of parameters passed by the GridSearchCV to the internal estimator i.e. the SVC. But whether or not they are used depends upon the SVC. In this case, the SVC doesnt throw any error, but also doesnt use the degree. You should print the scores of all those combinations of which you are doubtful. They should be equal. That will tell you that the degree parameter is unused.

Note: Make sure to set the random_state of the GridSearchCV to duplicate the tests.

Explanation: The work of GridSearchCV is to just pass the parameters, the train data to estimator for fitting, then use the test data for scoring, and results the parameter combinations which resulted in best score.

When an incompatible combination of parameter is passed to an estimator, it is dependent on the implementation, whether the parameters are ignored or an error is raised for it.

For eg. in LogisticRegression, there are two parameters:

penalty : str, ‘l1’ or ‘l2’, default: ‘l2’ 
         Used to specify the norm used in the penalization.

solver : {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’}, default: ‘liblinear’. 
         Algorithm to use in the optimization problem.
        ‘newton-cg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty.

As you can see, if I use l1 penalty with newton-cg solver, it results in incompatibility. So the estimator may chose to ignore the penalty paramter altogether or throw an error. In this case it throws an error.

GridSearchCV计算SVC与rbf内核有不同程度的？

1 个答案: