[pymvpa] Returning trained classifiers generated during cross-validation

[Tyson Aflalo]

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 <debian at onerussian.com>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
> > Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> >
> 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
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/attachments/20120108/589158d2/attachment.html>