def test_basic(self):
methods = [PageRank(), Diffusion(), Closeness(), HITS(), Harmonic()]
for adjacency in [test_graph(), test_digraph()]:
n = adjacency.shape[0]
for method in methods:
score = method.fit_transform(adjacency)
self.assertEqual(score.shape, (n, ))
self.assertTrue(score.min() >= 0)
def test_options(self):
ward = Ward()
ward_options = Ward(embedding_method=Spectral(3), co_cluster=True)
for algo in [ward, ward_options]:
for input_matrix in [test_graph(), test_digraph(), test_bigraph()]:
dendrogram = algo.fit_transform(input_matrix)
self.assertEqual(dendrogram.shape,
(input_matrix.shape[0] - 1, 4))
if algo.co_cluster:
self.assertEqual(algo.dendrogram_full_.shape,
(sum(input_matrix.shape) - 1, 4))
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_options(self):
for adjacency in [test_graph(), test_digraph()]:
n = adjacency.shape[0]
force_atlas = ForceAtlas()
layout = force_atlas.fit_transform(adjacency)
self.assertEqual((n, 2), layout.shape)
force_atlas = ForceAtlas(lin_log=True)
layout = force_atlas.fit_transform(adjacency)
self.assertEqual((n, 2), layout.shape)
force_atlas = ForceAtlas(approx_radius=1.)
layout = force_atlas.fit_transform(adjacency)
self.assertEqual((n, 2), layout.shape)
force_atlas.fit(adjacency, pos_init=layout, n_iter=1)
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)))