def test_lcc_numpy(self):
expected_lcc_matrix = np.array([
[0, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 1],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
])
expected_nodelist = np.array([0, 1, 2, 3, 5])
g = nx.DiGraph()
[g.add_node(i) for i in range(1, 7)]
g.add_edge(1, 2)
g.add_edge(1, 3)
g.add_edge(3, 4)
g.add_edge(3, 4)
g.add_edge(3, 6)
g.add_edge(6, 3)
g.add_edge(4, 2)
g = nx.to_numpy_array(g)
lcc_matrix, nodelist = gus.largest_connected_component(
g, return_inds=True)
np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix)
np.testing.assert_array_equal(nodelist, expected_nodelist)
lcc_matrix = gus.largest_connected_component(g)
np.testing.assert_array_equal(lcc_matrix, expected_lcc_matrix)
def test_lcc_scipy(self):
expected_lcc_matrix = np.array([
[0, 1, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 1],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
])
expected_nodelist = np.array([0, 1, 2, 3, 5])
adjacency = np.array([
[0, 1, 1, 0, 0, 0, 0], # connected
[0, 0, 0, 0, 0, 0, 0], # connected
[0, 0, 0, 1, 0, 1, 0], # connected
[0, 1, 0, 0, 0, 0, 0], # connected
[0, 0, 0, 0, 0, 0, 0], # not connected
[0, 0, 1, 0, 0, 0, 0], # connected
[0, 0, 0, 0, 0, 0, 0], # not connected
])
sparse_adjacency = csr_matrix(adjacency)
lcc_matrix, nodelist = gus.largest_connected_component(
sparse_adjacency, return_inds=True)
np.testing.assert_array_equal(lcc_matrix.toarray(),
expected_lcc_matrix)
np.testing.assert_array_equal(nodelist, expected_nodelist)
def test_lcc_scipy_empty(self):
adjacency = np.array([[0, 1], [1, 0]])
adjacency = csr_matrix(adjacency)
# remove the actual connecting edges. this is now a disconnected graph
# with two nodes. however, scipy still stores the entry that now has a 0 in it
# as having a 'nonempty' value, which is used in the lcc calculation
adjacency[0, 1] = 0
adjacency[1, 0] = 0
lcc_adjacency = gus.largest_connected_component(adjacency)
assert lcc_adjacency.shape[0] == 1
def test_lcc_bad_matrix(self):
A = np.array([0, 1])
with self.assertRaises(ValueError):
gus.largest_connected_component(A)