def test_disconnected_data(num_isolates, metric, force_approximation):
disconnected_data = np.random.choice(a=[False, True],
size=(10, 30),
p=[0.6, 1 - 0.6])
# Add some disconnected data for the corner case test
disconnected_data = np.vstack(
[disconnected_data,
np.zeros((num_isolates, 30), dtype="bool")])
new_columns = np.zeros((num_isolates + 10, num_isolates), dtype="bool")
for i in range(num_isolates):
new_columns[10 + i, i] = True
disconnected_data = np.hstack([disconnected_data, new_columns])
with pytest.warns(None) as w:
model = UMAP(
n_neighbors=3,
metric=metric,
force_approximation_algorithm=force_approximation,
).fit(disconnected_data)
assert len(w) >= 1 # at least one warning should be raised here
# we can't guarantee the order that the warnings will be raised in so check them all.
flag = 0
if num_isolates == 1:
warning_contains = "A few of your vertices"
elif num_isolates > 1:
warning_contains = "A large number of your vertices"
for wn in w:
flag += warning_contains in str(wn.message)
isolated_vertices = disconnected_vertices(model)
assert flag == 1, str(([wn.message for wn in w], isolated_vertices))
# Check that the first isolate has no edges in our umap.graph_
assert isolated_vertices[10] == True
number_of_nan = np.sum(np.isnan(model.embedding_[isolated_vertices]))
assert number_of_nan >= num_isolates * model.n_components
def test_disconnected_data_precomputed(num_isolates, sparse):
disconnected_data = np.random.choice(a=[False, True],
size=(10, 20),
p=[0.66, 1 - 0.66])
# Add some disconnected data for the corner case test
disconnected_data = np.vstack(
[disconnected_data,
np.zeros((num_isolates, 20), dtype="bool")])
new_columns = np.zeros((num_isolates + 10, num_isolates), dtype="bool")
for i in range(num_isolates):
new_columns[10 + i, i] = True
disconnected_data = np.hstack([disconnected_data, new_columns])
dmat = pairwise_special_metric(disconnected_data)
if sparse:
dmat = csr_matrix(dmat)
model = UMAP(n_neighbors=3, metric="precomputed",
disconnection_distance=1).fit(dmat)
# Check that the first isolate has no edges in our umap.graph_
isolated_vertices = disconnected_vertices(model)
assert isolated_vertices[10] == True
number_of_nan = np.sum(np.isnan(model.embedding_[isolated_vertices]))
assert number_of_nan >= num_isolates * model.n_components