def build_within_group_matrix(graph,
group_node_list,
group_tag,
precompute=False,
labels_as_ids=False):
result_mat = []
adj_mat = nx.adjacency_matrix(graph).todense()
global_node_list = list(graph.nodes())
if labels_as_ids:
global_node_list = [graph.node[n]['label'] for n in global_node_list]
indices = [global_node_list.index(n) for n in group_node_list]
result_mat = adj_mat[indices, :]
result_mat = result_mat[:, indices]
if precompute:
entity = EntityNet.from_raw_matrix(scipy.sparse.csr_matrix(result_mat),
group_tag, group_node_list)
else:
entity = EntityNet(scipy.sparse.csr_matrix(result_mat), group_tag,
group_node_list)
return entity
def test_entity_net_is_sparse_returns_false_for_numpy_matrix(self):
e = EntityNet(self.net_a,
'Net',
self.node_names,
precomputed=self.net_a_precomp)
self.assertFalse(e.is_sparse())
def test_entity_net_is_sparse_returns_true_for_csr_matrix(self):
sp = sparse.csr_matrix(self.net_a)
e = EntityNet(sp,
'Net',
self.node_names,
precomputed=self.net_a_precomp)
self.assertTrue(e.is_sparse())
def _create_good_graphdataset(self):
ent_a = EntityNet(self.net_a, "net_a", self.node_names,
self.net_a_precomp)
ent_b = EntityNet(self.net_b, "net_b", self.node_names_b,
self.net_b_precomp)
rel = RelationNet.from_raw_matrix(self.rel_ab, "rel_ab")
connections = np.matrix([[-1, 0], [-1, -1]])
dataset = GraphDataSet([ent_a, ent_b], [rel], connections)
return dataset
def test_subset_nodes_precompute_calls_precompute(self, mock_entity_init,
mock_precompute_matrix):
a = EntityNet(self.net_a,
self.name,
self.node_names,
precomputed=self.net_a_precomp)
subset = a.subset([1, 2], precompute=True)
mock_precompute_matrix.assert_called()
mock_entity_init.assert_called()
def test_subset_nodes(self):
a = EntityNet(self.net_a,
self.name,
self.node_names,
precomputed=self.net_a_precomp)
subset = a.subset([1, 2], precompute=False)
self.assertEqual(subset.matrix.shape, (2, 2))
self.assertEqual(subset.node_names, ['a1', 'a2'])
self.assertEqual(subset.matrix[0, 0], 0.00)
self.assertEqual(subset.matrix[0, 1], 0.00)
self.assertEqual(subset.matrix[1, 0], 0.00)
self.assertEqual(subset.matrix[1, 1], 0.00)
def test_graphdataset_densify_generates_dense_matrices(self):
ent_a = EntityNet(self.net_a, "net_a", self.node_names,
self.net_a_precomp)
ent_b = EntityNet(self.net_b, "net_b", self.node_names_b,
self.net_b_precomp)
rel = RelationNet.from_raw_matrix(self.rel_ab, "rel_ab")
connections = np.matrix([[-1, 0], [-1, -1]])
dataset = GraphDataSet([ent_a, ent_b], [rel],
connections,
densify=True)
self.assertFalse(sparse.issparse(dataset.networks[0].matrix))
self.assertFalse(sparse.issparse(dataset.networks[1].matrix))
self.assertFalse(sparse.issparse(dataset.relations[0].matrix))
def test_inconsistency_length_names_matrix_dims_raises_exception(self):
new_names = ['a0']
with self.assertRaises(ValueError):
a_from_raw = EntityNet(self.net_a,
self.name,
new_names,
precomputed=self.net_a_precomp)
def test_densify_keep_dense_matrix(self):
a_from_raw = EntityNet.from_raw_matrix(self.net_a, self.name,
self.node_names)
type_before = type(a_from_raw.matrix)
a_from_raw.densify()
type_after = type(a_from_raw.matrix)
self.assertEqual(type_before, type_after)
def test_densify_changes_sparse_matrix_to_dense(self):
a_from_raw = EntityNet.from_raw_matrix(sparse.csr_matrix(self.net_a),
self.name, self.node_names)
type_before = type(a_from_raw.matrix)
a_from_raw.densify()
type_after = type(a_from_raw.matrix)
self.assertNotEqual(type_before, type_after)
def test_read_write_consistency(self):
matfile = 'testmat.mat'
ent_a = EntityNet(self.net_a, "net_a", self.node_names,
self.net_a_precomp)
ent_b = EntityNet(self.net_b, "net_b", self.node_names_b,
self.net_b_precomp)
rel = RelationNet.from_raw_matrix(self.rel_ab, "rel_ab")
connections = np.matrix([[-1, 0], [-1, -1]])
dataset = GraphDataSet([ent_a, ent_b], [rel], connections)
dataset.write(self.test_dir, matfile)
new_dataset = GraphDataSet.read(self.test_dir, matfile)
self.assertEqual(len(new_dataset.networks), len(dataset.networks))
self.assertEqual(len(new_dataset.relations), len(dataset.relations))
self.assertEqual(new_dataset.connections.shape,
dataset.connections.shape)
def test_entity_net_from_raw_non_squared_raises_exception(self):
with self.assertRaises(ValueError):
e = EntityNet.from_raw_matrix(self.rel_ab, 'Net', self.node_names)
def test_entity_net_from_raw_no_errors(self):
e = EntityNet.from_raw_matrix(self.net_a, 'Net', self.node_names)
def test_dims_difference_precomputed_matrix_raise_exception(self):
with self.assertRaises(ValueError):
a_from_raw = EntityNet(self.net_a,
self.name,
self.node_names,
precomputed=self.net_b_precomp)
def test_non_squared_matrix_shape_raises_exception(self):
with self.assertRaises(ValueError):
entity = EntityNet(self.rel_ab,
self.name,
self.node_names,
precomputed=self.net_b_precomp)
def test_non_matching_names_length_matrix_shape_raises_exception(self):
with self.assertRaises(ValueError):
entity = EntityNet(self.net_b,
self.name,
self.node_names,
precomputed=self.net_b_precomp)