def test_biclustering(self):
self.directed: sparse.csr_matrix = painters()
bispectral_clustering = BiSpectralClustering(embedding_dimension=3,
co_clustering=False)
bispectral_clustering.fit(self.directed)
self.assertEqual(bispectral_clustering.labels_.shape[0],
self.directed.shape[0])
self.assertTrue(bispectral_clustering.col_labels_ is None)
def test_maxrdiff(self):
adjacency: sparse.csr_matrix = painters()
adj_array_seeds = -np.ones(adjacency.shape[0])
adj_array_seeds[:2] = np.arange(2)
adj_dict_seeds = {0: 0, 1: 1}
md = MaxDiff()
labels1 = md.fit_transform(adjacency, adj_array_seeds)
labels2 = md.fit_transform(adjacency, adj_dict_seeds)
self.assertTrue(np.allclose(labels1, labels2))
self.assertEqual(labels2.shape[0], adjacency.shape[0])
def test_multidiff(self):
adjacency: sparse.csr_matrix = painters()
adj_array_seeds = -np.ones(adjacency.shape[0])
adj_array_seeds[:2] = np.arange(2)
adj_dict_seeds = {0: 0, 1: 1}
md = MultiDiff()
membership1 = md.fit_transform(adjacency, adj_array_seeds)
membership2 = md.fit_transform(adjacency, adj_dict_seeds)
self.assertTrue(np.allclose(membership1, membership2))
self.assertEqual(membership2.shape, (adjacency.shape[0], 2))
def test_directed(self):
self.painters = painters(return_labels=False)
self.louvain.fit(self.painters)
labels = self.louvain.labels_
self.assertEqual(labels.shape, (14, ))
self.assertAlmostEqual(modularity(self.painters, labels), 0.32, 2)
self.bilouvain.fit(self.painters)
n1, n2 = self.painters.shape
row_labels = self.bilouvain.row_labels_
col_labels = self.bilouvain.col_labels_
self.assertEqual(row_labels.shape, (n1, ))
self.assertEqual(col_labels.shape, (n2, ))
def test_directed(self):
self.painters = painters(return_labels=False)
adjacency: sparse.csr_matrix = self.painters
self.diffusion.fit(adjacency, {0: 0, 1: 1, 2: 0.5})
score = self.diffusion.scores_
self.assertTrue(np.all(score <= 1 + self.tol) and np.all(score >= 0 - self.tol))
def setUp(self):
self.undirected: sparse.csr_matrix = karate_club()
self.directed: sparse.csr_matrix = painters()
self.bipartite: sparse.csr_matrix = movie_actor()