Hi Yarik, actually your post showed me something new (as almost all your replies do). I wasn't aware of the MappedClassifier-class, very nice. But this doesn'T solve my problem.
My reason for looping manually is to have the possibility of logging progress information and aving live-information whether the performance is reasonable. Some callback mechanism would help also. The ChainMapper approach doesn't help me, though, as I want zscoring pervoxel. I haven'T seen such a parameter for the ZScoreMapper. Greetings, Thorsten 2011/2/25 Yaroslav Halchenko <[email protected]>: >> # Here the two mapped datasets should be zscored equally, but with > > by 'equally' do you mean that mean/std should be computed on > concatenation of two datasets? > > if it would be ok with you if mean/std gets computed based on training > portion of the dataset, then why not simply construct > MappedClassifier chaining your two mappers together? e.g. something like > (not tested) > > clfr = MappedClassifier(SVM(), > mapper=ChainMapper([MyCustomMapper(...), > ZScoreMapper(...)])) > > and then simply doing > > cvte = CrossValidatedTransferError(TransferError(clfr), > NFoldSplitter(nperlabel="equal"), > enable_states = ['confusion']) > > error = cvte(dataset) > print cvte.states.confusion > > ? > > On Fri, 25 Feb 2011, Thorsten Kranz wrote: > >> Hi all, > >> I think this should be trivial, but maybe I'm not seeing the forest >> for the trees. > >> I want to do a cross-validation, I do this manually, also involving a >> custom mapper, by (simplified Example): > >> cm = ConfusionMatrix() >> clfr = SVM() >> for d1, d2 in NFoldSplitter(nperlabel="equal")(dataset): >> mcm = MyCustomMapper(...) >> mcm.train(d1) >> d1_mapped = mcm.forward(d1) >> d2_mapped = mcm.forward(d2) >> # Here the two mapped datasets should be zscored equally, but with >> perchunk=False, pervoxel=True >> ??? >> clfr.train(d1_mapped) >> cm.add(clfr.predict(d2_mapped.samples),d2_mapped.labels) > >> I don't see how to zscore both mapped datasets equally AND >> pervoxel=True, perchunk=False. I would love to use the ZScoreMapper >> for that, but it doesn'T provide the "per*" arguments. And the method >> zscore does accept parameters mean and std, but doesn't return them >> when called so I could reuse them in a second call. > >> I guess I will write my own zscore-method, just copy-pasting it from >> your source, but returning mean and std. > >> Or do you have another / better proposal? > >> Greetings, >> Thorsten > >> _______________________________________________ >> Pkg-ExpPsy-PyMVPA mailing list >> [email protected] >> http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa > > > -- > =------------------------------------------------------------------= > 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
