Ah, works perfectly. So it should be: datasets = list(partitioner.generate(dataset)) then print datasets[n].summary(chunks_attr='partitions')
I took a look and indeed I end up with individual test runs with empty conditions, the same way Tren pointed out in the previous message. But Balancer does work as advertised for the training set, because I don't have empty conditions when multiple runs are summed together... Thanks! -Edmund On Thu, Jul 26, 2012 at 2:40 PM, Michael Hanke <[email protected]> wrote: > On Thu, Jul 26, 2012 at 02:16:50PM -0400, Edmund Chong wrote: > > foo1 = partitioner.generate(dataset) > > You could to > > datasets = list(partitioner.generate(dataset)) > > to get them all at once. > > > > print foo2.summary() > > > > ...I tried that, but it gives me a summary table of the number of samples > > per condition per chunk. How do I view the samples per condition per > > partition (classification fold)? > > That a look at the datasets. They have a sample attribute 'partition' > (or something like that). Now look at the help for Dataset.summary() > (just do ``ds.summary?`` in IPython). That will list a 'chunks_attr' > arguments that you can set to be the partitions argument. Which in turn > will cause the summary to be per-partition. > > Hope that helps, > > Michael > > > -- > Michael Hanke > http://mih.voxindeserto.de > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
_______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
