I was creating a custom rbf function for the SVC class of sklearn as following:
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)
The shape of X_train is (396, 10), y_train is (396, 10) and X_test is (132, 10). However, when I try to run it, I get an error saying:
ValueError: X.shape[1] = 3960 should be equal to 396, the number of samples at training time
It seems the errors are due to the difference in the dimension of X_test and X_train, but is there any way to fix this error?
Thank you in advance!
CodePudding user response:
Your rbf kernel is written incorrectly. You need to return a matrix that is (n_samples, n_samples). In your code you basically unravelled everything, hence the error. You can refer to the actual code for rbf_kernel used by sklearn , and if we insert that it will work:
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