def test_cosmo_dataset(fn):
skip_if_no_external('scipy', min_version='0.8')
mat = _create_small_mat_dataset_dict()
ds_mat = cosmo.from_any(mat)
savemat(fn, mat)
# test Dataset, filename, dict in matlab form, and input from loadmat
for input in (ds_mat, fn, mat, loadmat(fn)):
# check dataset creation
ds = cosmo.from_any(mat)
# ensure dataset has expected vlaues
assert_array_equal(ds.samples, mat['samples'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.a.keys(), ['name'])
assert_array_equal(ds.a.name, 'input')
assert_array_equal(ds.sa.chunks, [2, 2])
assert_array_equal(ds.sa.targets, [1, 2])
assert_array_equal(ds.sa.labels, ['yin', 'yan'])
assert_array_equal(ds.fa.i, [3, 2, 1])
assert_array_equal(ds.fa.j, [1, 2, 2])
# check mapping to matlab format
mat_mapped = cosmo.map2cosmo(ds)
for m in (mat, mat_mapped):
assert_array_equal(ds_mat.samples, m['samples'])
_assert_ds_mat_attributes_equal(ds_mat, m)
def test_cosmo_repr_and_str():
# simple smoke test for __repr__ and __str__
creators = (_create_small_mat_nbrhood_dict, _create_small_mat_dataset_dict)
for creator in creators:
obj = cosmo.from_any(creator())
for fmt in 'rs':
obj_str = (("%%%s" % fmt) % obj)
assert_true(obj.__class__.__name__ in obj_str)
def test_cosmo_repr_and_str():
# simple smoke test for __repr__ and __str__
creators = (_create_small_mat_nbrhood_dict, _create_small_mat_dataset_dict)
for creator in creators:
obj = cosmo.from_any(creator())
for fmt in 'rs':
obj_str = (("%%%s" % fmt) % obj)
assert_true(obj.__class__.__name__ in obj_str)
def test_cosmo_io_h5py(fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
# Dataset from cosmo
ds = cosmo.from_any(_create_small_mat_dataset_dict())
h5save(fn, ds)
ds_loaded = h5load(fn)
_assert_ds_equal(ds, ds_loaded)
# Queryengine
qe = cosmo.from_any(_create_small_mat_nbrhood_dict())
h5save(fn, qe)
qe_loaded = h5load(fn)
assert_array_equal(qe.ids, qe_loaded.ids)
_assert_array_collectable_equal(qe.a, qe_loaded.a)
_assert_array_collectable_equal(qe.fa, qe_loaded.fa)
def test_cosmo_io_h5py(fn):
skip_if_no_external('h5py')
from mvpa2.base.hdf5 import h5save, h5load
# Dataset from cosmo
ds = cosmo.from_any(_create_small_mat_dataset_dict())
h5save(fn, ds)
ds_loaded = h5load(fn)
_assert_ds_equal(ds, ds_loaded)
# Queryengine
qe = cosmo.from_any(_create_small_mat_nbrhood_dict())
h5save(fn, qe)
qe_loaded = h5load(fn)
assert_array_equal(qe.ids, qe_loaded.ids)
_assert_array_collectable_equal(qe.a, qe_loaded.a)
_assert_array_collectable_equal(qe.fa, qe_loaded.fa)
def test_cosmo_searchlight():
ds = cosmo.from_any(_create_small_mat_dataset_dict())
sl = cosmo.CosmoSearchlight(mean_feature(),
_create_small_mat_nbrhood_dict())
ds_count = sl(ds)
dict_count = Dataset(samples=ds_count.samples,
fa=dict(k=arr([4, 3, 2, 1])),
sa=dict((k, ds.sa[k].value) for k in ds.sa.keys()),
a=dict(name=['output']))
_assert_ds_less_or_equal(dict_count, ds_count)
def test_cosmo_searchlight():
ds = cosmo.from_any(_create_small_mat_dataset_dict())
sl = cosmo.CosmoSearchlight(mean_feature(),
_create_small_mat_nbrhood_dict())
ds_count = sl(ds)
dict_count = Dataset(samples=ds_count.samples,
fa=dict(k=arr([4, 3, 2, 1])),
sa=dict((k, ds.sa[k].value) for k in ds.sa.keys()),
a=dict(name=['output']))
_assert_ds_less_or_equal(dict_count, ds_count)
def test_cosmo_dataset(fn):
skip_if_no_external('scipy', min_version='0.8')
mat = _create_small_mat_dataset_dict()
ds_mat = cosmo.from_any(mat)
savemat(fn, mat)
# test Dataset, filename, dict in matlab form, and input from loadmat
for input in (ds_mat, fn, mat, loadmat(fn)):
# check dataset creation
ds = cosmo.from_any(mat)
# ensure dataset has expected vlaues
assert_array_equal(ds.samples, mat['samples'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.a.keys(), ['name', 'size'])
assert_array_equal(ds.a.name, 'input')
assert_array_equal(ds.a.size, [3, 2, 1])
assert_array_equal(ds.sa.chunks, [2, 2])
assert_array_equal(ds.sa.targets, [1, 2])
assert_array_equal(ds.sa.labels, ['yin', 'yan'])
assert_array_equal(ds.fa.i, [3, 2, 1])
assert_array_equal(ds.fa.j, [1, 2, 2])
for convert_tuples in (True, False):
ds_copy = ds.copy(deep=True)
if convert_tuples:
# use dataset with tuple data
ds_copy.a.size = tuple(ds_copy.a.size)
# check mapping to matlab format
mat_mapped = cosmo.map2cosmo(ds_copy)
for m in (mat, mat_mapped):
assert_array_equal(ds_mat.samples, m['samples'])
_assert_ds_mat_attributes_equal(ds_mat, m)
def test_cosmo_queryengine(fn):
skip_if_no_external('scipy', min_version='0.8')
nbrhood_mat = _create_small_mat_nbrhood_dict()
neighbors = nbrhood_mat['neighbors']
savemat(fn, nbrhood_mat)
# test dict in matlab form, filename, and through QueryEngine loader
for input in (nbrhood_mat, fn, cosmo.CosmoQueryEngine.from_mat(neighbors)):
qe = cosmo.from_any(input)
assert_array_equal(qe.ids, [0, 1, 2, 3])
for i in qe.ids:
nbr_fids_base0 = neighbors[0, i][0] - 1
assert_array_equal(qe.query_byid(i), nbr_fids_base0)
_assert_ds_mat_attributes_equal(qe, nbrhood_mat, ('fa', 'a'))
def test_cosmo_queryengine(fn):
skip_if_no_external('scipy', min_version='0.8')
nbrhood_mat = _create_small_mat_nbrhood_dict()
neighbors = nbrhood_mat['neighbors']
savemat(fn, nbrhood_mat)
# test dict in matlab form, filename, and through QueryEngine loader
for input in (nbrhood_mat, fn, cosmo.CosmoQueryEngine.from_mat(neighbors)):
qe = cosmo.from_any(input)
assert_array_equal(qe.ids, [0, 1, 2, 3])
for i in qe.ids:
nbr_fids_base0 = neighbors[0, i][0] - 1
assert_array_equal(qe.query_byid(i), nbr_fids_base0)
_assert_ds_mat_attributes_equal(qe, nbrhood_mat, ('fa', 'a'))
