[pymvpa] Pattern localization

[Yaroslav Halchenko]

Sorry about the delay Matthias, we have been traveling a bit ;)
See comments embedded:

> I am trying to create a "discriminative map" which shows which features  
> are driving the classifiers performance. The procedure (below) will give  
> me one map per Nfold. My idea was, to calculate a mean map of those maps  
> and (may be) threshold it at an arbitrary level.
As for mean map, the easiest way is to use SplitClassifier and its
sensitivity map which is by default is composed by taking mean
sensitivity from all splits. Something like what we have done in
http://www.frontiersin.org/neuroinformatics/paper/10.3389/neuro.11/003.2009/
look into supplementals for full code / comments. But it would look like
(clf can well be yours FeatureSelection one)

        sclf = SplitClassifier(clf, splitter,
            enable_states=['confusion', 'training_confusion'])

        sensitivities = sclf.getSensitivityAnalyzer(
            # do not combine sensitivities across splits, nor across classes
            combiner=None, slave_combiner=None)(ds)

which would give you non-meaned set of sensitivities. If you like to have
them meaned, just use 'combiner=FirstAxisMean', or in current version just
don't specify combiner at all -- Mean value will be taken as the default
behavior (it should change in upcoming versions where we would not impose such
defaults -- better be explicit ;))


> The alternative I was thinking about, is a "stability analysis", that  
> picks voxels which have consistent clf weights across cross-validation.

Well -- with feature selection it gets trickier since some features might
not be selected in some of the splits, so you have a choice of judging
stability may be just by the portion of the splits where this feature was
selected at all

> What do you think would be an appropriate way (for linear classifiers)  
> of visualizing voxels which most strongly affect classifier predictions?

so -- either those raw (mean) sensitivities, or their stability assessment
across splits (just make sure that you normalized your features prior doing
that, with zscore or smth else).  If there is no feature selection, than,
permutation testing F- or converted z-scores might be also a nice one.
In both cases though you need to decide on the thresholding scheme ;)

I hope this helps
-- 
Yaroslav Halchenko
Research Assistant, Psychology Department, Rutgers-Newark
Student  Ph.D. @ CS Dept. NJIT
Office: (973) 353-1412 | FWD: 82823 | Fax: (973) 353-1171
        101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102
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