sklearn SVM 自定义内核引发 ValueError: X.shape[0] 应该等于 X.shape[1]

Question

我正在尝试实现一个自定义内核，即指数卡方内核，作为参数传递给 sklearn svm 函数，但是当我运行它时，会引发后续错误： ValueError: X.shape[0] 应该等于 X.shape[1]

我阅读了 numpy 函数执行的广播操作以加快计算速度，但我无法管理错误。

代码是：

import numpy as np
from sklearn import svm, datasets

# import the iris dataset (http://en.wikipedia.org/wiki/Iris_flower_data_set)
iris = datasets.load_iris()
train_features = iris.data[:, :2]  # Here we only use the first two features.
train_labels = iris.target


def my_kernel(x, y):
    gamma = 1
    return np.exp(-gamma * np.divide((x - y) ** 2, x + y))


classifier = svm.SVC(kernel=my_kernel)

classifier = classifier.fit(train_features, train_labels)

print "Train Accuracy : " + str(classifier.score(train_features, train_labels))

有什么帮助吗？

Answer 1

我相信卡方内核已经为您实现（from sklearn.metrics.pairwise import chi2_kernel）。

像这样

from functools import partial

from sklearn import svm, datasets
from sklearn.metrics.pairwise import chi2_kernel

# import the iris dataset (http://en.wikipedia.org/wiki/Iris_flower_data_set)
iris = datasets.load_iris()
train_features = iris.data[:, :2]  # Here we only use the first two features.
train_labels = iris.target

my_chi2_kernel = partial(chi2_kernel, gamma=1)

classifier = svm.SVC(kernel=my_chi2_kernel)

classifier = classifier.fit(train_features, train_labels)

print("Train Accuracy : " + str(classifier.score(train_features, train_labels)))

=====================

编辑：

所以问题实际上是关于如何实现卡方内核。我对此的看法是：-

def my_chi2_kernel(X):
    gamma = 1
    nom = np.power(X[:, np.newaxis] - X, 2)
    denom = X[:, np.newaxis] + X
    # NOTE: We need to fix some entries, since division by 0 is an issue here.
    #       So we take all the index of would be 0 denominator, and fix them.
    zero_denom_idx = denom == 0
    nom[zero_denom_idx] = 0
    denom[zero_denom_idx] = 1

    return np.exp(-gamma * np.sum(nom / denom, axis=len(X.shape)))

所以本质上，OP 的尝试中的 x - y 和 x + y 是错误的，因为它不是成对的减法或加法。

奇怪的是，自定义版本似乎比 sklearn 的 cythonized 版本更快（至少对于小型数据集？）