我使用以下代码（来源）来连接多个特征提取方法。

from sklearn.pipeline import Pipeline, FeatureUnionfrom sklearn.model_selection import GridSearchCVfrom sklearn.svm import SVCfrom sklearn.datasets import load_irisfrom sklearn.decomposition import PCAfrom sklearn.feature_selection import SelectKBestiris = load_iris()X, y = iris.data, iris.targetpca = PCA(n_components=2)selection = SelectKBest(k=1)# Build estimator from PCA and Univariate selection:combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])# Use combined features to transform dataset:X_features = combined_features.fit(X, y).transform(X)print("Combined space has", X_features.shape[1], "features")svm = SVC(kernel="linear")# Do grid search over k, n_components and C:pipeline = Pipeline([("features", combined_features), ("svm", svm)])param_grid = dict(features__pca__n_components=[1, 2, 3],                  features__univ_select__k=[1, 2],                  svm__C=[0.1, 1, 10])grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5, verbose=10)grid_search.fit(X, y)print(grid_search.best_estimator_)

我想从上述代码中获取所选特征的名称。

为此，我使用了grid_search.best_estimator_.support_。然而，这返回了一个错误，显示：

AttributeError: 'Pipeline' object has no attribute 'support_'

在Python的sklearn中，有没有办法获取上述代码中显示的选定特征名称？

如果需要，我很乐意提供更多详情。

回答：

这是我了解best_estimator_使用的最终特征的方法

>>> features = grid_search.best_estimator_.named_steps['features']# number of components chosen from pca>>> pca=features.transformer_list[0][1]>>> pca.n_components3# features chosen by selectKbest>>> select_k_best=features.transformer_list[1][1]>>> select_k_best.get_support()array([False, False,  True, False])