def test_remove_node(num_nodes, idx_to_remove, expected_adj):
my_graph = Graph(f'gr{num_nodes}', f'gr{num_nodes}.gxl', num_nodes)
nodes = []
for i in range(num_nodes):
tmp_node = Node(i, LabelNodeLetter(1+i, 1))
nodes.append(tmp_node)
my_graph.add_node(tmp_node)
for idx_start, idx_end in combinations(range(num_nodes), 2):
tmp_edge = Edge(idx_start, idx_end, LabelEdge(0))
my_graph.add_edge(tmp_edge)
my_graph.remove_node_by_idx(idx_to_remove)
# nodes.pop(0)
# expected edges
# expected_adjacency_mat = np.array([[0, 1, 1],
# [1, 0, 1],
# [1, 1, 0]])
print(f'--{nodes}')
print(my_graph.get_nodes())
# assert my_graph.get_nodes() == nodes
assert len(my_graph) == num_nodes - 1
def test_mutagenicity_with_deleted_nodes(mutagenicity_graphs, dataframe_mutagenicity, graph_name_source_target):
gr_name_src, gr_name_trgt = ['mutagen/' + name for name in graph_name_source_target]
graph_name_source, graph_name_target = graph_name_source_target
graph_source = [graph for graph in mutagenicity_graphs if graph.name == graph_name_source][0]
graph_target = [graph for graph in mutagenicity_graphs if graph.name == graph_name_target][0]
cst_cost_node = 11.0
cst_cost_edge = 1.1
ged = GED(EditCostMutagenicity(cst_cost_node, cst_cost_node,
cst_cost_edge, cst_cost_edge, 'dirac'))
# Reproduce the source graph with more nodes
new_gr_src = Graph(gr_name_src, 'gr.xls', len(graph_source)+2)
for node in graph_source.nodes:
new_gr_src.add_node(node)
for idx, edges in graph_source.get_edges().items():
for edge in edges:
if edge is None:
continue
new_gr_src.add_edge(edge)
new_gr_src.add_node(Node(len(graph_source), LabelNodeMutagenicity('C')))
new_gr_src.add_node(Node(len(graph_source)+1, LabelNodeMutagenicity('N')))
# Add random Edges
for _ in range(4):
new_gr_src.add_edge(Edge(len(graph_source),
random.randint(0, len(graph_source)-1),
LabelEdge(0)))
for _ in range(6):
new_gr_src.add_edge(Edge(len(graph_source) + 1,
random.randint(0, len(graph_source)),
LabelEdge(0)))
new_gr_src.remove_node_by_idx(len(graph_source))
new_gr_src.remove_node_by_idx(len(graph_source))
results = ged.compute_edit_distance(new_gr_src, graph_target)
expected = dataframe_mutagenicity.loc[gr_name_src, gr_name_trgt]
# import numpy as np
# np.savetxt(f'_c_{"X".join(graph_name_source_target)}.csv', np.asarray(ged.C), fmt='%10.3f', delimiter=';')
# np.savetxt(f'c_star_{"X".join(graph_name_source_target)}.csv', np.asarray(ged.C_star), fmt='%10.3f', delimiter=';')
print(f'###### diff {results - expected}')
print(f'{graph_name_source_target}: new dist {results} - old dist {expected}')
print(f'exp {expected}')
assert results == expected
def test_with_deleted_node(ged):
gr_src = Graph('gr1', 'gr1.gxl', 3)
gr_trgt = Graph('gr2', 'gr2.gxl', 3)
gr_src = Graph('gr1', 'gr1.gxl', 5)
gr_src.add_node(Node(0, LabelNodeAIDS('O', 1, 1, 2., 2.)))
gr_src.add_node(Node(1, LabelNodeAIDS('C', 1, 1, 2., 2.)))
gr_src.add_node(Node(2, LabelNodeAIDS('O', 1, 1, 2., 2.)))
# print(gr_src)
gr_src.add_node(Node(3, LabelNodeAIDS('Cl', 1, 1, 2., 2.)))
gr_src.add_node(Node(4, LabelNodeAIDS('N', 1, 1, 2.4, 2.)))
gr_src.add_edge(Edge(0, 3, LabelEdge(0)))
gr_src.add_edge(Edge(1, 3, LabelEdge(0)))
gr_src.add_edge(Edge(4, 2, LabelEdge(0)))
gr_src.add_edge(Edge(3, 4, LabelEdge(0)))
gr_src.remove_node_by_idx(4)
gr_src.remove_node_by_idx(3)
print(gr_src)
gr_trgt.add_node(Node(0, LabelNodeAIDS('H', 1, 1, 2., 2.)))
gr_trgt.add_node(Node(1, LabelNodeAIDS('Ca', 1, 1, 2., 2.)))
gr_trgt.add_node(Node(2, LabelNodeAIDS('C', 1, 1, 2., 2.)))
gr_trgt.add_edge(Edge(0, 1, LabelEdge(0)))
gr_trgt.add_edge(Edge(1, 2, LabelEdge(0)))
gr_trgt.add_edge(Edge(2, 0, LabelEdge(0)))
dist = ged.compute_edit_distance(gr_src, gr_trgt)
print(ged.C.base)
print(ged.C_star.base)
expected_dist = 4.7
print(dist)
assert round(dist, 2) == expected_dist