def _test_output_dim_directed(self, method):
n_components = 4
embed = method(n_components=n_components, concat=True)
n = 10
M = 20
A = er_nm(n, M, directed=True) + 5
self.assertEqual(embed.fit_transform(A).shape, (n, 8))
self.assertEqual(embed.latent_left_.shape, (n, 4))
self.assertEqual(embed.latent_right_.shape, (n, 4))
def test_baseembed_er(self):
n_components = 4
embed = BaseSpectralEmbed(n_components=n_components)
n = 10
M = 20
A = er_nm(n, M) + 5
embed._reduce_dim(A)
self.assertEqual(embed.latent_left_.shape, (n, n_components))
self.assertTrue(embed.latent_right_ is None)
def _test_output_dim(self, method, *args, **kwargs):
n_components = 4
embed = method(n_components=n_components)
n = 10
M = 20
A = er_nm(n, M) + 5
embed._reduce_dim(A)
self.assertEqual(embed.latent_left_.shape, (n, 4))
self.assertTrue(embed.latent_right_ is None)
def test_omni_unconnected():
np.random.seed(4)
n = 100
m = 50
graphs = [er_nm(n, m) for _ in range(2)]
omni = OmnibusEmbed()
with pytest.warns(UserWarning):
omni.fit(graphs)
def test_baseembed_er_directed_concat(self):
n_components = 4
embed = BaseSpectralEmbed(n_components=n_components, concat=True)
n = 10
M = 20
A = er_nm(n, M, directed=True)
embed._reduce_dim(A)
out = embed.fit_transform(A)
self.assertEqual(out.shape, (n, 2 * n_components))
self.assertTrue(embed.latent_right_ is not None)
def _gen_mat_data(n: int = 20,
m: int = 20,
p: int = 0.50,
mat_type: str = 'sb',
binary: bool = False,
asfile: bool = True,
n_graphs: int = 1):
if binary is True:
wt = 1
else:
wt = np.random.uniform
mat_list = []
mat_file_list = []
for nm in range(n_graphs):
if mat_type == 'er':
mat = largest_connected_component(
symmetrize(
remove_loops(
er_nm(n,
m,
wt=np.random.uniform,
wtargs=dict(low=0, high=1)))))
elif mat_type == 'sb':
if p is None:
raise ValueError(
f"for mat_type {mat_type}, p cannot be None")
mat = largest_connected_component(
symmetrize(
remove_loops(
sbm(np.array([n]),
np.array([[p]]),
wt=wt,
wtargs=dict(low=0, high=1)))))
else:
raise ValueError(f"mat_type {mat_type} not recognized!")
mat_list.append(mat)
if asfile is True:
mat_path_tmp = tempfile.NamedTemporaryFile(mode='w+',
suffix='.npy',
delete=False)
mat_path = str(mat_path_tmp.name)
np.save(mat_path, mat)
mat_file_list.append(mat_path)
mat_path_tmp.close()
return {'mat_list': mat_list, 'mat_file_list': mat_file_list}
def test_unconnected_warning(self):
A = csr_matrix(er_nm(100, 10))
with self.assertWarns(UserWarning):
ase = AdjacencySpectralEmbed()
ase.fit(A)
def _gen_mat_data(n: int=20, m: int=20, p: int=0.50,
mat_type: str='sb', binary: bool=False,
asfile: bool=True, n_graphs: int=1,
lcc: bool=False, modality: str='func'):
if binary is True:
wt = 1
else:
wt = np.random.uniform
mat_list = []
mat_file_list = []
if n_graphs > 0:
for nm in range(n_graphs):
if mat_type == 'er':
mat = symmetrize(
remove_loops(er_nm(n, m, wt=np.random.uniform,
wtargs=dict(low=0, high=1))))
elif mat_type == 'sb':
if p is None:
raise ValueError(
f"for mat_type {mat_type}, p cannot be None")
mat = symmetrize(
remove_loops(sbm(np.array([n]), np.array([[p]]),
wt=wt, wtargs=dict(low=0,
high=1))))
else:
raise ValueError(f"mat_type {mat_type} not recognized!")
if lcc is True:
mat = largest_connected_component(mat)
mat_list.append(autofix(mat))
if asfile is True:
path_tmp = tempfile.NamedTemporaryFile(mode='w+',
suffix='.npy',
delete=False)
mat_path_tmp = str(path_tmp.name)
out_folder = f"{str(Path.home())}/test_mats"
os.makedirs(out_folder, exist_ok=True)
if modality == 'func':
mat_path = f"{out_folder}/graph_sub-999_modality-func_" \
f"model-corr_template-" \
f"MNI152_2mm_" \
f"parc_tol-6fwhm_hpass-" \
f"0Hz_" \
f"signal-mean_thrtype-prop_thr-" \
f"{round(random.uniform(0, 1),2)}.npy"
elif modality == 'dwi':
mat_path = f"{out_folder}/graph_sub-999_modality-func_" \
f"model-csa_template-" \
f"MNI152_2mm_tracktype-local_" \
f"traversal-det_minlength-30_" \
f"tol-5_thrtype-prop_thr-" \
f"{round(random.uniform(0, 1),2)}.npy"
shutil.copyfile(mat_path_tmp, mat_path)
np.save(mat_path, mat)
mat_file_list.append(mat_path)
path_tmp.close()
return {'mat_list': mat_list, 'mat_file_list': mat_file_list}
def test_baseembed(self):
embed = BaseSpectralEmbed(n_components=None)
n = 10
M = 20
A = er_nm(n, M) + 5
embed._reduce_dim(A)
def test_unconnected_warning(self):
A = er_nm(100, 10)
with pytest.warns(UserWarning):
ase = AdjacencySpectralEmbed()
ase.fit(A)
