非常好的问题,但是scikit-learn
目前不支持核逻辑回归和ANOVA核。
不过你可以自己实现。
针对ANOVA核的示例1:
import numpy as np
from sklearn.metrics.pairwise import check_pairwise_arrays
from scipy.linalg import cholesky
from sklearn.linear_model import LogisticRegression
def anova_kernel(X, Y=None, gamma=None, p=1):
X, Y = check_pairwise_arrays(X, Y)
if gamma is None:
gamma = 1. / X.shape[1]
diff = X[:, None, :] - Y[None, :, :]
diff **= 2
diff *= -gamma
np.exp(diff, out=diff)
K = diff.sum(axis=2)
K **= p
return K
K = anova_kernel(X)
R = cholesky(K, lower=False)
clf = LogisticRegression()
clf.fit(R[train], y_train)
preds = clf.predict(R[test])¨
关于 Nyström 的示例 2:
from sklearn.kernel_approximation import Nystroem
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
K_train = anova_kernel(X_train)
clf = Pipeline([
('nys', Nystroem(kernel='precomputed', n_components=100)),
('lr', LogisticRegression())])
clf.fit(K_train, y_train)
K_test = anova_kernel(X_test, X_train)
preds = clf.predict(K_test)