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  1. PO
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    <p>The short answer is "perhaps not". Did you try something like the below? </p> <p>Based on example at <a href="http://scikit-learn.org/stable/modules/svm.html" rel="nofollow">http://scikit-learn.org/stable/modules/svm.html</a>, you need something like:</p> <pre><code> 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) </code></pre> <p>from here on just copy bottom of <a href="http://scikit-learn.org/0.13/auto_examples/plot_roc.html" rel="nofollow">http://scikit-learn.org/0.13/auto_examples/plot_roc.html</a></p>
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    1. POPossible to make a ROC plot from SVM with precomputed kernel in scikit-learn?
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    1. POPossible to make a ROC plot from SVM with precomputed kernel in scikit-learn?
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    1. CORight, but the problem is you used `X_test`, which I can't create by virtue of each data point consisting of 4 distinct n-dimensional feature vectors that are combined in the kernel function. I can't split the data into training and testing, unless you're advocating creating two gram matrices, which scikit-learn actually forbids doing ("results will be unexpected").
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    2. COAH, forgot about that, will edit my answer.
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    3. COSo if I'm reading this correctly, it's perfectly acceptable to provide a gram matrix `Kt` to `predict_proba()` that is different from the gram matrix `K` used to train the SVM? (with the twist that the test vectors in `Kt` need to be compared against the training vectors)
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