Thanks for fixing this so quickly! I installed pymvpa-0.4.1 from its tarball on a Mac Os X 10.5.6.
To refresh from your version in git would it be: $ git clone git://git.debian.org/git/pkg-exppsy/pymvpa.git ? Also, thanks for your comments on the interpretation of remapping sens into original feature space: > so, sens itself is mapped back into original feature space, but I am not > sure what that would actually mean here (interpretations could vary) > since it effectively replaces (IIRC) singular values with > sensitivites, ie changes contribution of each of SVD components. Could we not interpret it as which elements of the original feature space are more influential on the classification? On 05/02/2009 18:08, "Yaroslav Halchenko" <[email protected]> wrote: > o o -- you've hit a bug ;) sorry about that > > I pushed a fix in my (yoh/master) branch in our git repository, > but what do you run it on (OS?) and from (package, git repository, > source tarball)? would you be able to use pymvpa directly from the > sources in git repository? > > More about fix: > > since this moment we have 2 separate kinds of analyzers (for > regular mapped or featureselection classifiers), but that is irrelevant > at this user level. But also I've added a state variable > clf_sensitivities for sensitivity analyzers of proxy-classifiers, so > now, in your case I have: > > clf=MappedClassifier(LinearCSVMC(), SVDMapper()) > sensana=clf.getSensitivityAnalyzer() > sensana.states.enable(['clf_sensitivities']) > sens=sensana(dataset) > print sens.shape > print sensana.clf_sensitivities.shape > > and if it is ran as a part of our svdclf example on sample dataset I > see: > > Dataset / float32 216 x 530 uniq: 12 chunks 2 labels > (530,) > (216, 1) > > > so, sens itself is mapped back into original feature space, but I am not > sure what that would actually mean here (interpretations could vary) > since it effectively replaces (IIRC) singular values with > sensitivites, ie changes contribution of each of SVD components. > > but sensana.clf_sensitivities is smth easy to make sense of -- that > would be the sensitivities of the 'slave' classifier (ie > LinearCSVMC) which was ran on remapped data... from it you could assess > what dimensions of eigenspace are more influential on classification > than the others > > On Thu, 05 Feb 2009, Sergi Costafreda wrote: > >> Hi all >> Just starting with PyMVPA - thanks for a great tool! >> I am trying to obtain a sensitivity analysis for a SVD+SVM mapped >> classifier, by adapting the code of sensanas.py. When I try to run >> (1) >>> clf=MappedClassifier(LinearCSVMC(), SVDMapper()) >> (2) >>> sensana=clf.getSensitivityAnalyzer() >> (3) >>> sens=sensana(data) >> Line (2) returns the following error: >>>>> "AttributeError: 'MappedClassifier' object has no attribute >> '_MappedClassifier__clf'" >> I'd be grateful for any help with this! >> Best, >> Sergi > >> _______________________________________________ >> Pkg-ExpPsy-PyMVPA mailing list >> [email protected] >> http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa > _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
