No oddities. I just thought I would pass along some concrete usage which might expose a mistake that I was unaware of... nothing worse than a mistake that returns reasonable but incorrect results.
Thanks again On Sun, Jan 8, 2012 at 7:03 PM, Yaroslav Halchenko <[email protected]>wrote: > > On Sun, 08 Jan 2012, Tyson Aflalo wrote: > > I happen to be using libsvm, so I am attempting to use option 2. From > > what I understand SplitClassifier is a meta-classifier, and so I can > > simply feed my previous classifier to SplitClassifier and feed that to > > CrossValidation. SplitClassifier than just provides a layer that can > save > > stuff out over the folds... I have a tenuous grasp but hopefully this > is > > basically correct. > > seems to be 100% identical to my comprehension of that beast ;) > > > Can you glance at the couple of lines below to verify > > that I am using SplitClassifier correctly? > > I think it looks all right -- have you spot some oddity which lead you > to ask this question? > > > Thanks for the help! > > > baseclf = LinearCSVMC() > > > svdmapper=SVDMapper() > > > get_SVD_sliced = lambda x: ChainMapper([svdmapper, > > StaticFeatureSelection(x)]) > > > metaclf = MappedClassifier(baseclf, get_SVD_sliced(slice(0, 15))) > > > sc = SplitClassifier(metaclf, enable_ca=['stats']) > > > cv = CrossValidation(sc, NFoldPartitioner(), > > errorfx=mean_mismatch_error, enable_ca=['stats','datasets']) > > > err = cv(ds) > > > # now to test the novel dataset on an example classifier > > > mean(sc.clfs[1].predict(ds2.samples) == ds2.targets) > > > On Sun, Jan 8, 2012 at 4:14 PM, Yaroslav Halchenko > > <[1][email protected]> wrote: > > > there are 2 ways: > > > 1. [available only in mvpa2] > > any RepeatedMeasure (including CrossValidation) takes argument > > 'callback': > > > callback : functor > > Optional callback to extract information from inside the > main > > loop of > > the measure. The callback is called with the input 'data', > the > > 'node' > > instance that is evaluated repeatedly and the 'result' of a > > single > > evaluation -- passed as named arguments (see labels in > quotes) > > for > > every iteration, directly after evaluating the node. > > > so there you could access anything you care about in the 'node', > which > > is > > classifier in this case > > > BUT because the same classifier instance gets reused through the > > iterations, > > you can't just "store" the classifier. you can deepcopy some of > them > > (e.g. > > the ones relying on swig-ed APIs, like libsvm, would not be > > deepcopy-able) > > > 2. SplitClassifier > > > That one behaves similarly to cross-validation (just access its > > .ca.stats to > > get results of cross-validation), but also operates on copies of > the > > originally > > provided classifier, so you could access all of them via .clfs > > attribute. > > > Helps? > > On Sun, 08 Jan 2012, Tyson Aflalo wrote: > > > > Is there a means of accessing each trained classifier that is > > generated as > > > part of a cross-validation analysis?� > > > > Thanks, > > > > tyson > > > > _______________________________________________ > > > Pkg-ExpPsy-PyMVPA mailing list > > > [2][email protected] > > > [3] > http://lists.alioth.debian.org/cgi-bin/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/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
