On Tue, Feb 24, 2009 at 01:35:03PM -0500, Per B. Sederberg wrote: > It's actually pretty slow and the time it takes scales (non-linearly) > with the number of features. I've had to do a feature selection step > first to get it in the range of acceptability. You are likely running > into memory issues, too (i.e., swapping quite a bit), but I could be > wrong. I'm not sure this speed issue is a problem of it being in R or > rather that the algorithm is slowish.
Memory seems to be fine -- between 15 and 25% of the 12 Gigs. > Can you try performing an easy feature selection of say 5-10K > features, first? One to try might be the new feature stability > measure I added that looks for the most stable features across runs > for each label (different from ANOVA): > > stab_enet = FeatureSelectionClassifier( > ENET(lm=1.0,max_steps=500,trace=False,normalize=False), > SensitivityBasedFeatureSelection( > CorrStability(), > FixedNElementTailSelector(5000,mode='select',tail='upper')), > descr="ENET on 5K best(CorrStability) features") Thanks for the code -- will try it now. Michael -- GPG key: 1024D/3144BE0F Michael Hanke http://apsy.gse.uni-magdeburg.de/hanke ICQ: 48230050 _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
