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_disconnect(self):
adjacency = test_graph_disconnect()
position = np.random.random((adjacency.shape[0], 2))
image = svg_graph(adjacency, position)
self.assertEqual(image[1:4], 'svg')
biadjacency = test_bigraph_disconnect()
image = svg_bigraph(biadjacency)
self.assertEqual(image[1:4], 'svg')
def test_noreg(self):
adjacency = test_graph_disconnect()
n = adjacency.shape[0]
spectral = Spectral(regularization=None, equalize=True)
with self.assertRaises(ValueError):
spectral.fit(adjacency)
spectral = Spectral(regularization=0.)
spectral.fit(adjacency)
spectral.predict(np.random.rand(n))
def test_random_projection(self):
for algo in [RandomProjection(), RandomProjection(random_walk=True)]:
adjacency = test_graph()
embedding = algo.fit_transform(adjacency)
self.assertEqual(embedding.shape[1], 2)
adjacency = test_digraph()
embedding = algo.fit_transform(adjacency)
self.assertEqual(embedding.shape[1], 2)
adjacency = test_graph_disconnect()
embedding = algo.fit_transform(adjacency)
self.assertEqual(embedding.shape[1], 2)
def test_louvain_hierarchy(self):
louvain = LouvainNE()
for adjacency in [
test_graph(),
test_graph_disconnect(),
test_digraph()
]:
self.assertTupleEqual(
louvain.fit_transform(adjacency).shape, (10, 2))
louvain.fit(test_bigraph())
self.assertTupleEqual(louvain.embedding_.shape, (6, 2))
def test_laplacian(self):
# regular Laplacian
spectral = LaplacianEmbedding(self.k, normalized=False)
embedding = spectral.fit_transform(self.adjacency)
self.assertAlmostEqual(np.linalg.norm(embedding.mean(axis=0)), 0)
spectral = LaplacianEmbedding(self.k, regularization=0)
with self.assertRaises(ValueError):
spectral.fit(test_bigraph())
with self.assertRaises(ValueError):
spectral.fit(test_digraph())
with self.assertRaises(ValueError):
spectral.fit(test_graph_disconnect())
with self.assertWarns(Warning):
n = self.k - 1
spectral.fit_transform(np.ones((n, n)))
def test_regular(self):
# regular Laplacian
spectral = Spectral(self.k, normalized_laplacian=False, barycenter=False, normalized=False)
embedding = spectral.fit_transform(self.adjacency)
self.assertAlmostEqual(np.linalg.norm(embedding.mean(axis=0)), 0)
error = np.abs(spectral.predict(self.adjacency[1]) - embedding[1]).sum()
self.assertAlmostEqual(error, 0)
spectral = Spectral(self.k, normalized_laplacian=False, regularization=0, equalize=True)
with self.assertRaises(ValueError):
spectral.fit(test_bigraph())
with self.assertRaises(ValueError):
spectral.fit(test_digraph())
with self.assertRaises(ValueError):
spectral.fit(test_graph_disconnect())
with self.assertWarns(Warning):
n = self.k - 1
spectral.fit_transform(np.ones((n, n)))
def test_disconnected(self):
adjacency = test_graph_disconnect()
betweenness = Betweenness()
with self.assertRaises(ValueError):
betweenness.fit(adjacency)
def test_check_connected(self):
with self.assertRaises(ValueError):
check_connected(test_graph_disconnect())