Hey Yarik, thanks a lot for your work. It's really nice to have such quick reactions for problems with a library.
The installation from source is not a problem, I am only testing this approach on one machine, so I won't have to do it an all computers where PyMVPA is installed. Actually, I used TreeClassifier already before, for classification of iEEG data, very multiclass-style with 16 class labels. These labels correspond to 16 different stimuli, and these come from four categories. For cross validation, I balanced the data to have equal number of trials for each class (due to artifact rejection this isn't the case in my dataset) using a custom splitter object. The first approach of course was straight SVM (SMLR didn't perform too well), but TreeClassifiers, first deciding on the category, later on the exact label, improved the results. Not by orders, but noticeably. What I try to do now is a little bit more complicated. Don't know if it makes sense, will have to try it tomorrow, but I will let you know if it works. Once again thank you for your support, greetings, Thorsten What 2011/2/15 Yaroslav Halchenko <[email protected]>: > > On Mon, 14 Feb 2011, Yaroslav Halchenko wrote: >> and GNB inability to train on a single label.... more tomorrow, >> meanwhile you can try something like > >> clf = GNB >> tclf = TreeClassifier(clf(), >> {"g3": ([3], SVM()), >> "g6": ([1,2,4], TreeClassifier(clf(), >> {"g1": ([1], SVM()), >> "g5": ([2,4],clf())}))}) > > upon inspection, unfortunately our schema for weighting per labels is > far from being optimal and for some reason is not even in effect for > libsvm bindings -- I will leave it for TODO. Meanwhile I have pushed > into maint/0.4 branch ability to not specify classifiers in such > trailing nodes as g3 (i.e. just having None there instead of SVM()), and > stability for GNB to be able to cope with just a single label. > > For you to do not mess with installing from sources you could try > following (if you have shogun available) on your installation of PyMVPA: > > tclf = TreeClassifier(sg.SVM(C=(-1., -3.)), > {"g3": ([3], SVM()), > "g6": ([1,2,4], TreeClassifier( > sg.SVM(C=(-1., -2)), > {"g1": ([1], SVM()), > "g5": ([2,4], SVM())}))}) > > which would use shogun's SVM implementation where balancing is necessary and > would balance using per-class C values. unfortunately, once again our fault, > due to use of dictionaries (which do not guarantee the order of keys), what C > value gets associated with what branch is although deterministic might not > corresponding to the -1/+1 labels assignments for the corresponding split. > That is why if you see that you are still getting 'winner takes all' situation > -- just swap -1, -3 and -1 ,-2 accordingly in specification of C values. > > We will fix it up to become more coherent in the next release: > https://github.com/PyMVPA/PyMVPA/issues/issue/40 > https://github.com/PyMVPA/PyMVPA/issues/issue/41 > > P.S. I am not sure what was your goal for TreeClassifier, but in my > experimenting with it, it did not provide any generalization improvement over > straight multiclass SVMs/SMLR. > > -- > =------------------------------------------------------------------= > Keep in touch www.onerussian.com > Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic > > _______________________________________________ > 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
