def test_adhocsearchlight_perm_testing(self):
# just a smoke test pretty much
ds = datasets['3dmedium'].copy()
#ds.samples += np.random.normal(size=ds.samples.shape)*10
mvpa2.seed()
ds.fa['voxel_indices'] = ds.fa.myspace
from mvpa2.mappers.fx import mean_sample
from mvpa2.clfs.stats import MCNullDist
permutator = AttributePermutator('targets', count=8,
limit='chunks')
distr_est = MCNullDist(permutator, tail='left',
enable_ca=['dist_samples'])
slargs = (kNN(1),
NFoldPartitioner(0.5,
selection_strategy='random',
count=9))
slkwargs = dict(radius=1, postproc=mean_sample())
sl_nodistr = sphere_m1nnsearchlight(*slargs, **slkwargs)
skip_if_no_external('scipy') # needed for null_t
sl = sphere_m1nnsearchlight(
*slargs,
null_dist=distr_est,
enable_ca=['null_t'],
reuse_neighbors=True,
**slkwargs
)
mvpa2.seed()
res_nodistr = sl_nodistr(ds)
mvpa2.seed()
res = sl(ds)
# verify that we at least got the same main result
# ah (yoh) -- null dist is estimated before the main
# estimate so we can't guarantee correspondence :-/
# assert_array_equal(res_nodistr, res)
# only resemblance (TODO, may be we want to get/setstate
# for rng before null_dist.fit?)
# and dimensions correspond
assert_array_equal(distr_est.ca.dist_samples.shape,
(1, ds.nfeatures, 8))
assert_array_equal(sl.ca.null_t.samples.shape,
(1, ds.nfeatures))
def test_adhocsearchlight_perm_testing(self):
# just a smoke test pretty much
ds = datasets['3dmedium'].copy()
#ds.samples += np.random.normal(size=ds.samples.shape)*10
mvpa2.seed()
ds.fa['voxel_indices'] = ds.fa.myspace
from mvpa2.mappers.fx import mean_sample
from mvpa2.clfs.stats import MCNullDist
permutator = AttributePermutator('targets', count=8, limit='chunks')
distr_est = MCNullDist(permutator,
tail='left',
enable_ca=['dist_samples'])
slargs = (kNN(1),
NFoldPartitioner(0.5, selection_strategy='random', count=9))
slkwargs = dict(radius=1, postproc=mean_sample())
sl_nodistr = sphere_m1nnsearchlight(*slargs, **slkwargs)
skip_if_no_external('scipy') # needed for null_t
sl = sphere_m1nnsearchlight(*slargs,
null_dist=distr_est,
enable_ca=['null_t'],
reuse_neighbors=True,
**slkwargs)
mvpa2.seed()
res_nodistr = sl_nodistr(ds)
mvpa2.seed()
res = sl(ds)
# verify that we at least got the same main result
# ah (yoh) -- null dist is estimated before the main
# estimate so we can't guarantee correspondence :-/
# assert_array_equal(res_nodistr, res)
# only resemblance (TODO, may be we want to get/setstate
# for rng before null_dist.fit?)
# and dimensions correspond
assert_array_equal(distr_est.ca.dist_samples.shape,
(1, ds.nfeatures, 8))
assert_array_equal(sl.ca.null_t.samples.shape, (1, ds.nfeatures))