def test_bipartite(self):
biadjacency = movie_actor(metadata=False)
n_row, n_col = biadjacency.shape
seeds_row_array = -np.ones(n_row)
seeds_row_array[:2] = np.arange(2)
seeds_row_dict = {0: 0, 1: 1}
seeds_col_dict = {0: 0}
classifiers = [
BiPageRankClassifier(),
BiDiffusionClassifier(),
BiKNN(embedding_method=GSVD(3), n_neighbors=1),
BiPropagation(),
BiDirichletClassifier()
]
for clf in classifiers:
clf.fit(biadjacency, seeds_row_array)
labels_row1, labels_col1 = clf.labels_row_, clf.labels_col_
clf.fit(biadjacency, seeds_row_dict)
labels_row2, labels_col2 = clf.labels_row_, clf.labels_col_
self.assertTrue(np.allclose(labels_row1, labels_row2))
self.assertTrue(np.allclose(labels_col1, labels_col2))
self.assertEqual(labels_col2.shape[0], n_col)
self.assertTupleEqual(clf.membership_row_.shape, (n_row, 2))
self.assertTupleEqual(clf.membership_col_.shape, (n_col, 2))
clf.fit(biadjacency, seeds_row_dict, seeds_col_dict)
def test_bipartite(self):
self.bipartite: sparse.csr_matrix = movie_actor(return_labels=False)
bispectral_ward = BiSpectralWard(embedding_dimension=3)
bispectral_ward.fit(self.bipartite)
self.assertEqual(bispectral_ward.row_dendrogram_.shape,
(self.bipartite.shape[0] - 1, 4))
self.assertEqual(bispectral_ward.col_dendrogram_.shape,
(self.bipartite.shape[1] - 1, 4))
def test_co_clustering(self):
self.bipartite: sparse.csr_matrix = movie_actor()
n1, n2 = self.bipartite.shape
bispectral_clustering = BiSpectralClustering(embedding_dimension=3)
bispectral_clustering.fit(self.bipartite)
self.assertEqual(bispectral_clustering.row_labels_.shape[0], n1)
self.assertEqual(bispectral_clustering.col_labels_.shape[0], n2)
self.assertEqual(bispectral_clustering.labels_.shape[0], n1 + n2)
def test_bimaxdiff(self):
biadjacency: sparse.csr_matrix = movie_actor()
biadj_array_seeds = -np.ones(biadjacency.shape[0])
biadj_array_seeds[:2] = np.arange(2)
biadj_dict_seeds = {0: 0, 1: 1}
bmd = BiMaxDiff()
labels1 = bmd.fit_transform(biadjacency, biadj_array_seeds)
labels2 = bmd.fit_transform(biadjacency, biadj_dict_seeds)
self.assertTrue(np.allclose(labels1, labels2))
self.assertEqual(labels2.shape[0], biadjacency.shape[0])
def test_bipartite(self):
bipagerank = BiPageRank()
biadjacency = movie_actor()
n1, n2 = biadjacency.shape
bipagerank.fit(biadjacency, {0: 1})
row_scores = bipagerank.row_scores_
self.assertEqual(len(row_scores), n1)
col_scores = bipagerank.col_scores_
self.assertEqual(len(col_scores), n2)
scores = bipagerank.scores_
self.assertEqual(len(scores), n1 + n2)
def test_bimultidiff(self):
biadjacency: sparse.csr_matrix = movie_actor()
biadj_array_seeds = -np.ones(biadjacency.shape[0])
biadj_array_seeds[:2] = np.arange(2)
biadj_array_seeds = biadj_array_seeds
biadj_dict_seeds = {0: 0, 1: 1}
bmd = BiMultiDiff()
bmd.fit(biadjacency, biadj_array_seeds)
membership1 = bmd.membership_.copy()
bmd.fit(biadjacency, biadj_dict_seeds)
membership2 = bmd.membership_.copy()
self.assertTrue(np.allclose(membership1, membership2))
self.assertEqual(membership2.shape, (biadjacency.shape[0], 2))
def test_coneighbors(self):
biadjacency = movie_actor(metadata=False)
operator = CoNeighborOperator(biadjacency)
transition = normalize(operator)
x = transition.dot(np.ones(transition.shape[1]))
self.assertAlmostEqual(np.linalg.norm(x - np.ones(operator.shape[0])),
0)
operator.astype(np.float)
operator.right_sparse_dot(sparse.eye(operator.shape[1], format='csr'))
operator1 = CoNeighborOperator(biadjacency, normalized=False)
operator2 = CoNeighborOperator(biadjacency, normalized=False)
x = np.random.randn(operator.shape[1])
x1 = (-operator1).dot(x)
x2 = (operator2 * -1).dot(x)
x3 = operator1.T.dot(x)
self.assertAlmostEqual(np.linalg.norm(x1 - x2), 0)
self.assertAlmostEqual(np.linalg.norm(x2 - x3), 0)
def setUp(self):
self.bipartite = movie_actor(return_labels=False)
def setUp(self):
"""Simple biadjacency for tests."""
self.biadjacency = movie_actor()
def setUp(self):
self.undirected: sparse.csr_matrix = karate_club()
self.directed: sparse.csr_matrix = painters()
self.bipartite: sparse.csr_matrix = movie_actor()
def setUp(self):
"""Simple biadjacency for tests."""
self.biadjacency = movie_actor()
n_row, n_col = self.biadjacency.shape
self.slr = SparseLR(self.biadjacency, [(np.random.rand(n_row), np.random.rand(n_col))])
def setUp(self):
"""Simple biadjacency for tests."""
self.biadjacency = movie_actor()
n1, n2 = self.biadjacency.shape
self.slr = SparseLR(self.biadjacency,
[(np.random.rand(n1), np.random.rand(n2))])