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 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_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_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)