def test_spectral_biclustering():
# Test Kluger methods on a checkerboard dataset.
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=0)
non_default_params = {'method': ['scale', 'log'],
'svd_method': ['arpack'],
'n_svd_vecs': [20],
'mini_batch': [True]}
for mat in (S, csr_matrix(S)):
for param_name, param_values in non_default_params.items():
for param_value in param_values:
model = SpectralBiclustering(
n_clusters=3,
n_init=3,
init='k-means++',
random_state=0,
)
model.set_params(**dict([(param_name, param_value)]))
if issparse(mat) and model.get_params().get('method') == 'log':
# cannot take log of sparse matrix
with pytest.raises(ValueError):
model.fit(mat)
continue
else:
model.fit(mat)
assert model.rows_.shape == (9, 30)
assert model.columns_.shape == (9, 30)
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert consensus_score(model.biclusters_,
(rows, cols)) == 1
_test_shape_indices(model)
def test_spectral_biclustering():
# Test Kluger methods on a checkerboard dataset.
S, rows, cols = make_checkerboard((30, 30), 3, noise=0.5,
random_state=0)
non_default_params = {'method': ['scale', 'log'],
'svd_method': ['arpack'],
'n_svd_vecs': [20],
'mini_batch': [True]}
for mat in (S, csr_matrix(S)):
for param_name, param_values in non_default_params.items():
for param_value in param_values:
model = SpectralBiclustering(
n_clusters=3,
n_init=3,
init='k-means++',
random_state=0,
)
model.set_params(**dict([(param_name, param_value)]))
if issparse(mat) and model.get_params().get('method') == 'log':
# cannot take log of sparse matrix
assert_raises(ValueError, model.fit, mat)
continue
else:
model.fit(mat)
assert_equal(model.rows_.shape, (9, 30))
assert_equal(model.columns_.shape, (9, 30))
assert_array_equal(model.rows_.sum(axis=0),
np.repeat(3, 30))
assert_array_equal(model.columns_.sum(axis=0),
np.repeat(3, 30))
assert_equal(consensus_score(model.biclusters_,
(rows, cols)), 1)
_test_shape_indices(model)
