[pymvpa] balancing leave-one-out

[Ben Acland]

Thanks Dylan! I'll check this out tomorrow.

Ben

On Aug 18, 2014, at 11:49 PM, Dylan D. Wagner <wagner.1174 at osu.edu> wrote:

> Hi Ben,
> 
>  I did something nominally similar using Sifter (inspired by an earlier post from Yarick: http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/2012q4/002322.html). Here's how I think you can set up your partitioning scheme.
> 
> 
> # Make 4 dummy subjects and give them subject and group ids.
> ds_all = [normal_feature_dataset(nlabels=4, snr=100, perlabel=10, nfeatures = 5, nchunks=5) for _ in range(4)]
> for i in range(0,4,2):
>    ds_all[i].sa['subject'] = np.repeat('s%s' %i, len(ds_all[i]))
>    ds_all[i].sa['group'] = np.repeat('groupA', len(ds_all[i]))
> 
> for i in range(1,4,2):
>    ds_all[i].sa['subject'] = np.repeat('s%s' %i, len(ds_all[i]))
>    ds_all[i].sa['group'] = np.repeat('groupB', len(ds_all[i]))
> 
> # vstack the datasets
> ds_all = vstack(ds_all, a=0)
> 
> # Setup the partitioner
> npart = NFoldPartitioner(cvtype=2, attr='subject')
> 
> # Sift through the partitions excluding those where the test partition doesn't have one of each group.
> # Should scale up to more than one group id.
> sift = Sifter([('partitions',2), ('group', dict(uvalues=ds_all.sa['group'].unique, balanced=True))])
> 
> # Combine npart and sifter
> part = ChainNode([npart, sift], space = 'partitions')
> 
> # Check partitions
> for i, split in enumerate(list(part.generate(ds_all))):
>    print 'Partition 2, Split %d:' %i
>    print split[split.sa.partitions==2].sa['subject'].unique
>    print split[split.sa.partitions==2].sa['group'].unique
> 
> # And of course...
> clf = LinearCSVMC(space = 'group')
> 
> 
> 
> On 8/18/2014 10:46 PM, Ben Acland wrote:
>> Okay, forget that. Simpler version of the same problem:
>> 
>> I'd now like to leave out one subject from each group (I'm trying to decode 'sub_group'). Seems like it should be easy enough. Anyone have the quick answer on this?
>> 
>> Ben
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> 
> 
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