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. Can you glance at the couple of lines below to verify that I am using SplitClassifier correctly? 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 <[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 > > [email protected] > > > 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
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