scikit-learn中具有rbf内核的SVM使用递归功能消除的ValueError

我正在尝试在scikit-learn中使用递归功能消除（RFE）函数，但仍会收到错误消息ValueError: coef_ is only available when using a linear kernel。我正在尝试使用rbf内核为支持向量分类器（SVC）执行功能选择。网站上的这个示例执行得很好：

print(__doc__)

from sklearn.svm import SVC
from sklearn.cross_validation import StratifiedKFold
from sklearn.feature_selection import RFECV
from sklearn.datasets import make_classification
from sklearn.metrics import zero_one_loss

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000, n_features=25, n_informative=3,
                       n_redundant=2, n_repeated=0, n_classes=8,
                       n_clusters_per_class=1, random_state=0)

# Create the RFE object and compute a cross-validated score.
svc = SVC(kernel="linear")
rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, 2),
          scoring='accuracy')
rfecv.fit(X, y)

print("Optimal number of features : %d" % rfecv.n_features_)

# Plot number of features VS. cross-validation scores
import pylab as pl
pl.figure()
pl.xlabel("Number of features selected")
pl.ylabel("Cross validation score (nb of misclassifications)")
pl.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
pl.show()

但是，仅将内核类型从线性更改为rbf，如下所示，就会产生错误：

print(__doc__)

from sklearn.svm import SVC
from sklearn.cross_validation import StratifiedKFold
from sklearn.feature_selection import RFECV
from sklearn.datasets import make_classification
from sklearn.metrics import zero_one_loss

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000, n_features=25, n_informative=3,
                       n_redundant=2, n_repeated=0, n_classes=8,
                       n_clusters_per_class=1, random_state=0)

# Create the RFE object and compute a cross-validated score.
svc = SVC(kernel="rbf")
rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, 2),
          scoring='accuracy')
rfecv.fit(X, y)

print("Optimal number of features : %d" % rfecv.n_features_)

# Plot number of features VS. cross-validation scores
import pylab as pl
pl.figure()
pl.xlabel("Number of features selected")
pl.ylabel("Cross validation score (nb of misclassifications)")
pl.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
pl.show()

看来这可能是一个错误，但是如果有人发现我做错了，那就太好了。另外，我正在运行带有scikit-learn版本0.14.1的python 2.7.6。

谢谢您的帮助！