Dear PyMVPAers, in a searchlight analysis, I would like to be able to extract the individual predictions for each sample, on every cross-validated searchlight.
Why, I hear you ask? Currently I'm getting very marginal results for my classifier, and I would like to boost sensitivity by amalgamating results across the 20+ participants that I have access to. The idea would be to take a committee classification decision for each test sample, across the 20+ participants (e.g. by majority voting of the prediction, or a mean of a graded measure like the probabilities that a logistic regression gives). So far I've looked at the postproc argument of mvpa2.measures.searchlight.sphere_searchlight but don't seem to be able to dig down deeper than the foldwise error rates. On my reading the results_* parameters are not relevant to my question. Should I instead be writing a custom function to read out and record from each instance of the CrossValidation, with its postproc attribute (e.g. via CrossValidation.stats.sets)? My current classifier definition is this: > # searchlight classifier > searchLightSize = 3 > clf = PLR(); > > cv = CrossValidation(clf, NFoldPartitioner(), > enable_ca=['probabilities', 'samples_error','stats', > 'calling_time','confusion', 'estimates', 'predictions', > 'repetition_results', 'raw_results', 'null_prob']) > > sl = sphere_searchlight(cv, radius=searchLightSize, > postproc=mean_sample()) > Ideas and suggestions welcome! best, Brian _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
