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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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    <p>I'm using this example for creating ROC plot from SVM classification results: <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> <p>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:</p> <pre><code>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 </code></pre> <p>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 <code>predict_proba()</code> on the test set. <strong>Is it possible to do this in scikit-learn using a precomputed kernel?</strong></p>
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