def testSimple(self):
# bunch of datasets
datasets = [
Dataset(samples=N.arange(12).reshape((4,3)), labels=1),
Dataset(samples=N.zeros((4,4)), labels=1),
Dataset(samples=N.ones((4,2), dtype='float'), labels=1),
]
mds = MetaDataset(datasets)
# all together
self.failUnless(mds.samples.shape == (4, 9))
# should do upcasting
self.failUnless(mds.samples.dtype == 'float')
# simple samples attrs
self.failUnless((mds.labels == [1] * 4).all())
self.failUnless((mds.chunks == range(4)).all())
# do sample selection across all datasets
mds1 = mds.selectSamples([0,3])
self.failUnless(mds1.samples.shape == (2, 9))
self.failUnless(\
(mds1.samples[0] == [0, 1, 2, 0, 0, 0, 0, 1, 1]).all())
self.failUnless(\
(mds1.samples[1] == [9, 10, 11, 0, 0, 0, 0, 1, 1]).all())
# more tricky feature selection on all datasets
mds2 = mds.selectFeatures([1,4,8])
self.failUnless(\
(mds2.samples == [[ 1, 0, 1],
[ 4, 0, 1],
[ 7, 0, 1],
[10, 0, 1]] ).all())
def testMapping(self):
if not externals.exists('nifti'):
return
from mvpa.datasets.nifti import NiftiDataset
eeds = EEPDataset(os.path.join(pymvpa_dataroot, 'eep.bin'), labels=[1,2])
nids = NiftiDataset(os.path.join(pymvpa_dataroot, 'example4d.nii.gz'),
labels=[1,2])
plainds = Dataset(samples=N.arange(8).reshape((2,4)), labels=[1,2])
datasets = (eeds, plainds, nids)
mds = MetaDataset(datasets)
self.failUnless(mds.nfeatures == N.sum([d.nfeatures for d in datasets]))
self.failUnless(mds.nsamples == 2)
# try reverse mapping
mr = mds.mapReverse(N.arange(mds.nfeatures))
self.failUnless(len(mr) == 3)
self.failUnless(mr[1].shape == (plainds.nfeatures,))
