具有自定义内核的GridSearchCV

Question

我有以下代码：

import numpy as np
from sklearn import svm
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

def tanimotoKernel(xs, ys):
    a = 0
    b = 0
    for x, y in zip(xs, ys):
        a += min(x, y)
        b += max(x, y)
    return a / b

def tanimotoLambdaKernel(xs,ys, gamma = 0.01):
    return np.exp(gamma * tanimotoKernel(xs,ys)) / (np.exp(gamma) - 1)

def GramMatrix(X1, X2, K_function=tanimotoLambdaKernel):
    gram_matrix = np.zeros((X1.shape[0], X2.shape[0]))
    for i, x1 in enumerate(X1):
        for j, x2 in enumerate(X2):
            gram_matrix[i, j] = K_function(x1, x2)
    return gram_matrix

X, y = datasets.load_iris(return_X_y=True)
x_train, x_test, y_train, y_test = train_test_split(X, y)
clf.fit(x_train, y_train)
accuracy_score(clf.predict(x_test), y_test)

clf = svm.SVC(kernel=GramMatrix)

但是，我希望能够使用GridSearchCV调优tanimotoLambdaKernel的gamma参数，因为我不想手动测试参数、检查准确性等

有没有办法做到这一点

Answer 1

这似乎不可能直接实现；内置内核的参数都经过了烘焙

一种方法是自己传递不同的内核。这有点复杂，因为您的嵌套函数定义了内核，所以我使用partial：

from functools import partial
param_space = {
    kernel: [
        partial(
            GramMatrix,
            K_function=partial(
                tanimotoLambdaKernel,
                gamma=g,
            )
        )
        for g in <your list of gammas>
    ]
}

另一种方法是定制类。这在hyperparameter搜索中更清晰，因为“hyperparameter”可以是gamma，但在维护类时可能需要做更多的工作。在这种情况下，我通过重用gamma参数来避免重写__init__，并将内核设置为fit时间，以便set_params对gamma正常工作

class SVC_tanimoto(svm.SVC):
    """SVC with a Tanimoto kernel."""
    def fit(self, X, y, sample_weight=None):
        self.kernel = partial(
            GramMatrix,
            K_function=partial(
                tanimotoLambdaKernel,
                gamma=self.gamma,
            )
        )
        super().fit(X, y, sample_weight=sample_weight)
        return self