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_bilouvain(self):
biadjacency = star_wars()
adjacency = bipartite2undirected(biadjacency)
louvain = Louvain(modularity='newman')
labels1 = louvain.fit_transform(adjacency)
louvain.fit(biadjacency)
labels2 = np.concatenate((louvain.labels_row_, louvain.labels_col_))
self.assertTrue((labels1 == labels2).all())
def test_bimodularity(self):
biadjacency = star_wars()
labels_row = np.array([0, 0, 1, 1])
labels_col = np.array([0, 1, 0])
self.assertEqual(bimodularity(biadjacency, labels_row, labels_col),
0.1875)
with self.assertRaises(ValueError):
bimodularity(biadjacency, labels_row[:2], labels_col)
with self.assertRaises(ValueError):
bimodularity(biadjacency, labels_row, labels_col[:2])
def test_aggregate(self):
adjacency = house()
biadjacency = star_wars()
louvain = Louvain(return_aggregate=True)
louvain.fit(adjacency)
self.assertTrue(np.issubdtype(louvain.adjacency_.dtype, np.float_))
bilouvain = BiLouvain(return_aggregate=True)
bilouvain.fit(biadjacency)
self.assertTrue(np.issubdtype(bilouvain.biadjacency_.dtype, np.float_))
def test_options(self):
biadjacency = star_wars(metadata=False)
n_row, n_col = biadjacency.shape
min_dim = min(n_row, n_col) - 1
gsvd = GSVD(n_components=5,
regularization=0.,
solver='halko',
relative_regularization=True)
with self.assertWarns(Warning):
gsvd.fit(biadjacency)
self.assertEqual(gsvd.embedding_row_.shape, (n_row, min_dim))
self.assertEqual(gsvd.embedding_col_.shape, (n_col, min_dim))
gsvd = GSVD(n_components=1,
regularization=0.1,
solver='halko',
relative_regularization=True)
gsvd.fit(biadjacency)
gsvd.predict(np.random.rand(n_col))
def setUp(self):
"""Simple regularized adjacency and biadjacency for tests."""
self.undirected = SparseLR(house(), [(np.ones(5), np.ones(5))])
self.bipartite = SparseLR(star_wars(), [(np.ones(4), np.ones(3))])
def setUp(self):
"""Basic biadjacency for tests."""
self.biadjacency = star_wars()
