Possible to make a ROC plot from SVM with precomputed kernel in scikit-learn?

Question

I'm using this example for creating ROC plot from SVM classification results: http://scikit-learn.org/0.13/auto_examples/plot_roc.html

However, each data point effectively consists of 4 length-d feature vectors, combined using a custom kernel function that doesn't conform to the specific K(X, X) paradigm. As such, I have to supply a precomputed kernel to scikit-learn in order to do classification. It looks something like this:

K = numpy.zeros(shape = (n, n))

# w1 + w2 + w3 + w4 = 1.0

# v1: array, shape (n, d)
# w1: float in [0, 1)
chi = sklearn.metrics.pairwise.chi2_kernel(v1, v1)
mu = 1.0 / numpy.mean(chi)
K += w1 * numpy.exp(-mu * chi)

# v2: array, shape (n, d)
# w2: float in [0, 1)
chi = sklearn.metrics.pairwise.chi2_kernel(v2, v2)
mu = 1.0 / numpy.mean(chi)
K += w2 * numpy.exp(-mu * chi)

# v3: array, shape (n, d)
# w3: float in [0, 1)
chi = sklearn.metrics.pairwise.chi2_kernel(v3, v3)
mu = 1.0 / numpy.mean(chi)
K += w3 * numpy.exp(-mu * chi)

# v4: array, shape (n, d)
# w4: float in [0, 1)
chi = sklearn.metrics.pairwise.chi2_kernel(v4, v4)
mu = 1.0 / numpy.mean(chi)
K += w4 * numpy.exp(-mu * chi)

return K

The main obstacle to generating a ROC plot (from the above link) seems to be the process of splitting the data into two sets, and then calling predict_proba() on the test set. Is it possible to do this in scikit-learn using a precomputed kernel?

Answer 1

The short answer is "perhaps not". Did you try something like the below?

Based on example at http://scikit-learn.org/stable/modules/svm.html, you need something like:

    import numpy as np

    from sklearn import svm
    X = np.array([[0, 0], [1, 1]])
    y = [0, 1]
    clf = svm.SVC(kernel='precomputed')

    # kernel computation
    K = numpy.zeros(shape = (n, n))

    # "At the moment, the kernel values between all training vectors 
    #  and the test vectors must be provided." 
    #  according to scikit learn web page. 
    #  -- This is the problem!
    # v1: array, shape (n, d)
    # w1: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(v1, v1)
    mu = 1.0 / numpy.mean(chi)
    K += w1 * numpy.exp(-mu * chi)

    # v2: array, shape (n, d)
    # w2: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(v2, v2)
    mu = 1.0 / numpy.mean(chi)
    K += w2 * numpy.exp(-mu * chi)

    # v3: array, shape (n, d)
    # w3: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(v3, v3)
    mu = 1.0 / numpy.mean(chi)
    K += w3 * numpy.exp(-mu * chi)

    # v4: array, shape (n, d)
    # w4: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(v4, v4)
    mu = 1.0 / numpy.mean(chi)
    K += w4 * numpy.exp(-mu * chi)

    # scikit-learn is a wrapper LIBSVM and looking at the LIBSVM Readme file
    # it seems you need kernel values for test data something like this:    

    Kt = numpy.zeros(shape = (nt, n))
    # t1: array, shape (nt, d)
    # w1: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(t1, v1)
    mu = 1.0 / numpy.mean(chi)
    Kt += w1 * numpy.exp(-mu * chi)

    # v2: array, shape (n, d)
    # w2: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(t2, v2)
    mu = 1.0 / numpy.mean(chi)
    Kt += w2 * numpy.exp(-mu * chi)

    # v3: array, shape (n, d)
    # w3: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(t3, v3)
    mu = 1.0 / numpy.mean(chi)
    Kt += w3 * numpy.exp(-mu * chi)

    # v4: array, shape (n, d)
    # w4: float in [0, 1)
    chi = sklearn.metrics.pairwise.chi2_kernel(t4, v4)
    mu = 1.0 / numpy.mean(chi)
    Kt += w4 * numpy.exp(-mu * chi)

    clf.fit(K, y) 

    # predict on testing examples
    probas_ = clf.predict_proba(Kt)

from here on just copy bottom of http://scikit-learn.org/0.13/auto_examples/plot_roc.html