Sounds great!! Thank you Yaroslav!
Roberto. On 3 October 2011 19:38, Yaroslav Halchenko <[email protected]> wrote: > AHA -- that is a "cool" one ;) if only we refactored those selectors > into proper Node's, they could have been chained :-/ now you could > chain (afaik) the corresponding SensitivityBasedFeatureSelection's (or > just use CombinedFeatureSelection), but that would be wasteful > since it would compute ANOVA twice... also we could take advantage that > feature selectors are simple callables so we could craft a corresponding > funct(ion|tor) which would chain those two: > > def custom_tail_selector(seq): > seq1 = FractionTailSelector(0.01, mode='discard', > tail='upper')(seq) > seq2 = FractionTailSelector(0.05, mode='select', > tail='upper')(seq) > return list(set(seq1).intersection(seq2)) > > it seems to work as desired (pushed a rudimentary smoke unittest for it) > > On Mon, 03 Oct 2011, Roberto Guidotti wrote: > > > Thank you for the response, > > > I'm always lazy to improve problem description (and my english doesn't > > help me!!!) > > > Theoretically for every fold I have to select important features, so I > > perform ANOVA, the algorithm ranks my feature, but I want to drop the > > first 1% and use the 5% (or X%) of the new rank after 1% dropping. I > > found on my analysis that most important feature are, in the 90% of > > cases, on the brain edge (probably motion artefacts voxels that still > > affect the dataset also after preprocessing). > > > For this reason I would like to extract ANOVA ranking stack or to find > > a method to use voxels from 1.01% to 5.01%! > > > So I want to perform ANOVA on data, drop the first 1%, select the new > > ranking stack (5% of residual voxels) and train the classifier! > > > Thank you > > > RG > > > On 3 October 2011 18:14, Yaroslav Halchenko <[1][email protected] > > > > wrote: > > > I might be missing the problem since you seem to have to know the > > blocks > > you need ;) so following the example from the clfs warehouse: > > FeatureSelectionClassifier( > > kNN(), > > SensitivityBasedFeatureSelection( > > OneWayAnova(), > > FractionTailSelector(0.05, mode='select', tail='upper')), > > descr="kNN on 5%(ANOVA)") > > logical change would be to use 0.99 instead of 0.05 to select 99% > > top voxels > > (or in other words to dump 1%)... or just change mode to 'discard' > > and specify > > 0.01 as the fraction > > > On Mon, 03 Oct 2011, Roberto Guidotti wrote: > > > Hi all, > > > I would like to know if there is a method to extract the feature > > > ranking from ANOVA feature selection or if there is a method to > > drop > > > first 1% of voxel included in the ranking and select other voxel > > as > > > usual (with FractionTailSelection()). > > > Thank you > > > Roberto > > > > _______________________________________________ > > > Pkg-ExpPsy-PyMVPA mailing list > > > [2][email protected] > > > [3] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-expps > > y-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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