new_pop_list = []
for parent in pop:
child = self.crossover(parent, pop)
child = self.mutate(child)
new_pop_list.append(child)
self.population = new_pop_list
if __name__ == '__main__':
random.seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
torch.manual_seed(cmd_args.seed)
label_map, _, g_list = load_graphs()
base_classifier = load_base_model(label_map, g_list)
if cmd_args.idx_start + cmd_args.num_instances > len(g_list):
instances = g_list[cmd_args.idx_start:]
else:
instances = g_list[cmd_args.idx_start:cmd_args.idx_start +
cmd_args.num_instances]
attacked = 0.0
pbar = tqdm(instances)
idx = cmd_args.idx_start
for g in pbar:
agent = GeneticAgent(base_classifier, g, cmd_args.num_mod)
if len(agent.population) == 0:
continue
for i in range(cmd_args.rounds):
if len(added) >= num_added:
break
if len(added) == 0:
continue
g2 = g.copy()
g2.add_edges_from(added)
cand_list.append(S2VGraph(g2, s2v_g.label))
_, _, acc = model(cand_list)
acc = acc.double().cpu().numpy()
for i in range(len(cand_list)):
if acc[i] < 1.0:
return cand_list[i]
return cand_list[0]
if __name__ == '__main__':
random.seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
torch.manual_seed(cmd_args.seed)
label_map, train_glist, test_glist = load_er_data()
base_classifier = load_base_model(label_map, test_glist)
new_test_list = []
for g in tqdm(test_glist):
new_test_list.append(propose_attack(base_classifier, g))
test_graphs(base_classifier, new_test_list)