def test_cosine(self):
biadjacency = test_bigraph()
method = GSVD(3, solver='lanczos')
embedding = method.fit_transform(biadjacency)
embedding_col = method.embedding_col_
fit, div, modularity = cosine_modularity(biadjacency,
embedding,
embedding_col,
return_all=True)
modularity = cosine_modularity(biadjacency,
embedding,
embedding_col,
return_all=False)
self.assertAlmostEqual(modularity, fit - div)
adjacency = test_graph()
embedding = method.fit_transform(adjacency)
fit, div, modularity = cosine_modularity(adjacency,
embedding,
return_all=True)
self.assertAlmostEqual(modularity, fit - div)
with self.assertRaises(ValueError):
cosine_modularity(biadjacency, embedding)
louvain = LouvainEmbedding()
embedding = louvain.fit_transform(adjacency)
fit, div, modularity = cosine_modularity(adjacency,
embedding,
return_all=True)
self.assertAlmostEqual(modularity, fit - div)
def test_keywords(self):
biadjacency = test_bigraph()
n_row, n_col = biadjacency.shape
hits = HITS()
hits.fit(biadjacency)
self.assertEqual(hits.scores_row_.shape, (n_row, ))
self.assertEqual(hits.scores_col_.shape, (n_col, ))
def test_keywords(self):
biadjacency = test_bigraph()
n_row, n_col = biadjacency.shape
for hits in [HITS(solver='lanczos', tol=0.1), HITS(solver='halko', n_oversamples=1)]:
hits.fit(biadjacency)
self.assertEqual(hits.scores_row_.shape, (n_row,))
self.assertEqual(hits.scores_col_.shape, (n_col,))
def test_birandom_projection(self):
for algo in [
BiRandomProjection(),
BiRandomProjection(random_walk=True)
]:
biadjacency = test_bigraph()
embedding = algo.fit_transform(biadjacency)
self.assertEqual(embedding.shape[1], 2)
self.assertEqual(algo.embedding_col_.shape[1], 2)
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_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_predict(self):
louvain = LouvainEmbedding()
louvain.fit(test_graph())
embedding_vector = louvain.predict(
np.array([1, 0, 0, 0, 1, 1, 0, 0, 0, 1]))
self.assertEqual(embedding_vector.shape[0], 1)
bilouvain = BiLouvainEmbedding()
bilouvain.fit(test_bigraph())
embedding_vector = bilouvain.predict(np.array([1, 0, 0, 0, 1, 1]))
self.assertEqual(embedding_vector.shape[0], 1)
def test_bipartite(self):
biadjacency = test_bigraph()
n_row, n_col = biadjacency.shape
methods = [PageRank(), Diffusion(), HITS(), Katz(), Dirichlet()]
for method in methods:
method.fit(biadjacency)
scores_row = method.scores_row_
scores_col = method.scores_col_
self.assertEqual(scores_row.shape, (n_row, ))
self.assertEqual(scores_col.shape, (n_col, ))
def test_predict(self):
for method in ['remove', 'merge', 'keep']:
louvain = LouvainEmbedding(isolated_nodes=method)
louvain.fit(test_graph())
embedding_vector = louvain.predict(np.array([1, 0, 0, 0, 1, 1, 0, 0, 0, 1]))
self.assertEqual(embedding_vector.shape[0], 1)
for method in ['remove', 'merge', 'keep']:
bilouvain = BiLouvainEmbedding(isolated_nodes=method)
bilouvain.fit(test_bigraph())
embedding_vector = bilouvain.predict(np.array([1, 0, 0, 0, 1, 1]))
self.assertEqual(embedding_vector.shape[0], 1)
def test_copagerank(self):
seeds = {0: 1}
biadjacency = test_bigraph()
adjacency = co_neighbor_graph(biadjacency,
method='exact',
normalized=True)
scores1 = BiPageRank(damping_factor=0.85,
solver='lanczos').fit_transform(
biadjacency, seeds)
scores2 = PageRank(damping_factor=0.85**2,
solver='lanczos').fit_transform(adjacency, seeds)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0., places=6)
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)
biadjacency = test_bigraph()
embedding = algo.fit_transform(biadjacency)
self.assertEqual(embedding.shape[1], 2)
self.assertEqual(algo.embedding_col_.shape[1], 2)
def test_copagerank(self):
seeds = {0: 1}
biadjacency = test_bigraph()
adjacency = co_neighbors_graph(biadjacency, method='exact')
scores1 = CoPageRank().fit_transform(biadjacency, seeds)
scores2 = PageRank().fit_transform(adjacency, seeds)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0.)
adjacency = co_neighbors_graph(biadjacency.T.tocsr(), method='exact')
scores1 = CoPageRank().fit(biadjacency, seeds_col=seeds).scores_col_
scores2 = PageRank().fit_transform(adjacency, seeds)
self.assertAlmostEqual(np.linalg.norm(scores1 - scores2), 0.)
with self.assertRaises(ValueError):
CoPageRank(solver='diteration').fit_transform(biadjacency, seeds)
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_bilouvain_hierarchy(self):
blne = BiHLouvainEmbedding()
blne.fit(test_bigraph())
self.assertTupleEqual(blne.embedding_.shape, (6, 2))