def test_permute_graph(self):
# Does not have to be DAG
matrix = np.array([[1, 1, 0], [0, 0, 1], [1, 0, 1]])
labels = ['a', 'b', 'c']
p1, l1 = graph_util.permute_graph(matrix, labels, [2, 0, 1])
self.assertTrue(
np.array_equal(p1, np.array([[0, 1, 0], [0, 1, 1], [1, 0, 1]])))
self.assertEqual(l1, ['b', 'c', 'a'])
p1, l1 = graph_util.permute_graph(matrix, labels, [0, 2, 1])
self.assertTrue(
np.array_equal(p1, np.array([[1, 0, 1], [1, 1, 0], [0, 1, 0]])))
self.assertEqual(l1, ['a', 'c', 'b'])
def test_random_isomorphism_hashing(self):
# Tests that hash_module always provides the same hash for randomly
# generated isomorphic graphs.
for _ in range(1000):
# Generate random graph. Note: the algorithm works (i.e. same hash ==
# isomorphic graphs) for all directed graphs with coloring and does not
# require the graph to be a DAG.
size = random.randint(3, 20)
matrix = np.random.randint(0, 2, [size, size])
labels = [random.randint(0, 10) for _ in range(size)]
# Generate permutation of matrix and labels.
perm = np.random.permutation(size).tolist()
pmatrix, plabels = graph_util.permute_graph(matrix, labels, perm)
# Hashes should be identical.
hash1 = graph_util.hash_module(matrix, labels)
hash2 = graph_util.hash_module(pmatrix, plabels)
self.assertEqual(hash1, hash2)
def test_is_isomorphic(self):
# Reuse some tests from hash_module
matrix1 = np.array(
[[0, 1, 1, 0,],
[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 0]])
label1 = [-1, 1, 2, -2]
label2 = [-1, 2, 1, -2]
self.assertTrue(graph_util.is_isomorphic((matrix1, label1),
(matrix1, label2)))
# Simple graph with edge permutation
matrix1 = np.array(
[[0, 1, 1, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]])
label1 = [-1, 1, 2, 3, -2]
matrix2 = np.array(
[[0, 1, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]])
label2 = [-1, 2, 3, 1, -2]
matrix3 = np.array(
[[0, 1, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]])
label3 = [-1, 2, 1, 3, -2]
self.assertTrue(graph_util.is_isomorphic((matrix1, label1),
(matrix2, label2)))
self.assertTrue(graph_util.is_isomorphic((matrix1, label1),
(matrix3, label3)))
self.assertFalse(graph_util.is_isomorphic((matrix1, label1),
(matrix2, label1)))
# Connected non-isomorphic regular graphs on 6 interior vertices (8 total)
matrix1 = np.array(
[[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0]])
matrix2 = np.array(
[[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0]])
label1 = [-1, 1, 1, 1, 1, 1, 1, -2]
self.assertFalse(graph_util.is_isomorphic((matrix1, label1),
(matrix2, label1)))
# Connected isomorphic regular graphs on 8 total vertices (bipartite)
matrix1 = np.array(
[[0, 0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 1, 0, 1],
[0, 0, 0, 0, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0]])
matrix2 = np.array(
[[0, 1, 0, 1, 1, 0, 0, 0],
[1, 0, 1, 0, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 0, 1, 0],
[1, 0, 1, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 1, 0, 1],
[0, 1, 0, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 0, 1, 0, 1],
[0, 0, 0, 1, 1, 0, 1, 0]])
label1 = [1, 1, 1, 1, 1, 1, 1, 1]
# Sanity check: manual permutation
perm = [0, 5, 7, 2, 4, 1, 3, 6]
pm1, pl1 = graph_util.permute_graph(matrix1, label1, perm)
self.assertTrue(np.array_equal(matrix2, pm1))
self.assertEqual(pl1, label1)
self.assertTrue(graph_util.is_isomorphic((matrix1, label1),
(matrix2, label1)))
label2 = [1, 1, 1, 1, 2, 2, 2, 2]
label3 = [1, 2, 1, 2, 2, 1, 2, 1]
self.assertTrue(graph_util.is_isomorphic((matrix1, label2),
(matrix2, label3)))
