Sklearn SVM 自定义 rbf 核函数

Question

我正在为 sklearn 的 SVC class 创建自定义 rbf 函数，如下所示：

def rbf_kernel(x, y, gamma):
    dis = np.sqrt(((x.reshape(-1, 1)) - y.reshape(1, -1)) ** 2)
    return np.exp(-(gamma*dis)**2)


def eval_kernel(kernel):
    model = SVC(kernel=kernel, C=C, gamma=gamma, degree=degree, coef0=coef0)
    model.fit(X_train, y_train)
    X_test_predict = model.predict(X_test)
    acc = (X_test_predict == y_test).sum() / y_test.shape[0]
    return acc

for k1, k2 in [('rbf', lambda x, y: rbf_kernel(x, y, gamma))]:
    acc1 = eval_kernel(k1)
    acc2 = eval_kernel(k2)

    assert(abs(acc1 - acc2) < eps)

X_train的形状是(396, 10)，y_train是(396, 10)，X_test是(132, 10)。但是，当我尝试运行它时，我收到一条错误消息：

ValueError: X.shape[1] = 3960 should be equal to 396, the number of samples at training time

看来错误是由于X_test和X_train的维度不同造成的，但是有什么办法可以解决这个错误吗？

提前致谢！

Answer 1

你的 rbf 内核写错了。您需要 return 一个矩阵，即 (n_samples, n_samples)。在您的代码中，您基本上解开了所有内容，因此出现了错误。您可以参考 actual code for rbf_kernel used by sklearn ，如果我们插入它将起作用：

from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

X,y = make_classification(528)

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25)

def my_kernel(X, Y, gamma=0.1):
    K = euclidean_distances(X, Y, squared=True)
    K *= -gamma
    np.exp(K, K)  # exponentiate K in-place
    return K

def eval_kernel(kernel):
    model = SVC(kernel=kernel,gamma=0.1)
    model.fit(X_train, y_train)
    X_test_predict = model.predict(X_test)
    acc = (X_test_predict == y_test).sum() / y_test.shape[0]
    return acc

eval_kernel('rbf')
0.8409090909090909

eval_kernel(my_kernel)
0.8409090909090909

Sklearn SVM 自定义 rbf 核函数

Sklearn SVM custom rbf kernel function

python

numpy

machine-learning

scikit-learn

svm