def test_depend_views(data):
mvmds = MVMDS()
fit = mvmds.fit_transform(data['dep_views'])
for i in range(fit.shape[0]):
for j in range(fit.shape[1]):
assert math.isnan(fit[i,j])
def test_fit_transform_values(data):
mvmds = MVMDS(len(data['samp_views'][0]))
comp = mvmds.fit_transform(data['samp_views'])
comp2 = np.array([[-0.81330129, 0.07216426, 0.57735027],
[0.34415456, -0.74042171, 0.57735027],
[0.46914673, 0.66825745, 0.57735027]])
for i in range(comp.shape[0]):
for j in range(comp.shape[1]):
assert comp[i, j] - comp2[i, j] < .000001
def test_fit_transform_values(data):
n_components = len(data['samp_views'][0])
mvmds = MVMDS(n_components = n_components)
comp = mvmds.fit_transform(data['samp_views'])
comp2 = np.array([[-0.81330129, 0.07216426, 0.5773503],
[0.34415456, -0.74042171, 0.5773503],
[0.46914673, 0.66825745, 0.5773503]])
# Last component calculation varies across Python implementations.
np.testing.assert_almost_equal(
np.abs(comp[:,:n_components-1]),
np.abs(comp2[:,:n_components-1])
)
def test_dissimilarity_precomputed_euclidean(data):
test_views = []
for i in data['samp_views']:
test_views.append(euclidean_distances(i))
mvmds1 = MVMDS(dissimilarity='euclidean')
mvmds2 = MVMDS(dissimilarity='precomputed')
fit1 = mvmds1.fit_transform(data['samp_views'])
fit2 = mvmds2.fit_transform(test_views)
np.testing.assert_almost_equal(np.abs(fit2), np.abs(fit1))
def test_fit_transform_values_neg():
with pytest.raises(ValueError):
MVMDS(n_components=-4)
def test_fit_transformdifferent_wrong_samples(data):
with pytest.raises(ValueError):
mvmds = MVMDS()
mvmds.fit_transform(data['wrong_views'])
def test_component_num_greater(data):
mvmds = MVMDS(n_components = len(data['random_views'][0] + 1))
comp = mvmds.fit_transform(data['random_views'])
assert len(comp) == len(data['random_views'][0])
def test_dissimilarity_euclidean():
with pytest.raises(ValueError):
MVMDS(n_components=-3)
def test_dissimilarity_wrong():
with pytest.raises(ValueError):
MVMDS(dissimilarity=3)
def test_num_iter_value_fail():
with pytest.raises(ValueError):
MVMDS(num_iter=0)
def test_fit_transform_values_0(data):
with pytest.raises(ValueError):
mvmds = MVMDS(n_components=0)
comp = mvmds.fit_transform(data['samp_views'])
def check_num_iter(data):
with pytest.raises(ValueError):
mvmds = MVMDS(n_components=-3)
comp = mvmds.fit_transform(data['samp_views'])