def test_is_bipartite(self):
biadjacency = star_wars(metadata=False)
adjacency = bipartite2undirected(biadjacency)
self.assertTrue(is_bipartite(adjacency))
bipartite, pred = is_bipartite(adjacency, return_biadjacency=True)
self.assertEqual(bipartite, True)
self.assertEqual(np.all(biadjacency.data == pred.data), True)
adjacency = sparse.identity(2, format='csr')
bipartite, biadjacency = is_bipartite(adjacency,
return_biadjacency=True)
self.assertEqual(bipartite, False)
self.assertIsNone(biadjacency)
adjacency = directed2undirected(cyclic_digraph(3))
bipartite, biadjacency = is_bipartite(adjacency,
return_biadjacency=True)
self.assertEqual(bipartite, False)
self.assertIsNone(biadjacency)
with self.assertRaises(ValueError):
is_bipartite(cyclic_digraph(3))
self.assertTrue(~is_bipartite(sparse.eye(3)))
def test_largest_cc(self):
adjacency = cyclic_digraph(3)
adjacency += adjacency.T
largest_cc, indices = largest_connected_component(adjacency,
return_labels=True)
self.assertAlmostEqual(
np.linalg.norm(largest_cc.toarray() -
np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])), 0)
self.assertEqual(np.linalg.norm(indices - np.array([0, 1, 2])), 0)
biadjacency = star_wars(metadata=False)
largest_cc, indices = largest_connected_component(biadjacency,
return_labels=True)
self.assertAlmostEqual(
np.linalg.norm(
largest_cc.toarray() -
np.array([[1, 0, 1], [1, 0, 0], [1, 1, 1], [0, 1, 1]])), 0)
self.assertEqual(np.linalg.norm(indices[0] - np.array([0, 1, 2, 3])),
0)
self.assertEqual(np.linalg.norm(indices[1] - np.array([0, 1, 2])), 0)
self.assertTrue(
isinstance(
largest_connected_component(adjacency, return_labels=False),
sparse.csr_matrix))
def test_scaling(self):
adjacency = cyclic_digraph(3)
with self.assertRaises(ValueError):
check_scaling(-1, adjacency, regularize=True)
adjacency = test_graph_disconnect()
with self.assertRaises(ValueError):
check_scaling(-1, adjacency, regularize=False)
def test_is_acyclic(self):
adjacency_with_self_loops = sparse.identity(2, format='csr')
self.assertFalse(is_acyclic(adjacency_with_self_loops))
directed_cycle = cyclic_digraph(3)
self.assertFalse(is_acyclic(directed_cycle))
undirected_line = linear_graph(2)
self.assertFalse(is_acyclic(undirected_line))
acyclic_graph = linear_digraph(2)
self.assertTrue(is_acyclic(acyclic_graph))
def test_dir2undir(self):
n = 3
adjacency = cyclic_digraph(n)
ref = directed2undirected(adjacency)
self.assertEqual(ref.shape, adjacency.shape)
self.assertTrue(is_symmetric(ref))
adjacency = house()
n = adjacency.shape[0]
error = 0.5 * directed2undirected(adjacency) - adjacency
self.assertEqual(error.nnz, 0)
slr = SparseLR(adjacency, [(np.zeros(n), np.zeros(n))])
slr = 0.5 * directed2undirected(slr)
self.assertEqual(slr.shape, (n, n))
x = np.random.randn(n)
error = np.linalg.norm(slr.dot(x) - adjacency.dot(x))
self.assertAlmostEqual(error, 0)
def setUp(self) -> None:
"""Load graph for tests."""
self.adjacency = cyclic_digraph(3)
def setUp(self):
"""Simple graphs for tests."""
self.adjacency = cyclic_digraph(3)
self.dense_mat = np.identity(3)
def test_shortest_paths(self):
with self.assertRaises(ValueError):
shortest_path(cyclic_digraph(3), [0, 1], [0, 1])
