def eval(self):
self.env.setup(deepcopy(self.test_g_list))
t = 0
while not self.env.isTerminal():
list_at = self.make_actions(t, greedy=True)
self.env.step(list_at)
t += 1
test_loss = loop_dataset(env.g_list, env.classifier,
list(range(len(env.g_list))))
print('\033[93m average test: loss %.5f acc %.5f\033[0m' %
(test_loss[0], test_loss[1]))
if cmd_args.phase == 'train' and self.best_eval is None or test_loss[
1] < self.best_eval:
print(
'----saving to best attacker since this is the best attack rate so far.----'
)
torch.save(self.net.state_dict(),
cmd_args.save_dir + '/epoch-best.model')
with open(cmd_args.save_dir + '/epoch-best.txt', 'w') as f:
f.write('%.4f\n' % test_loss[1])
self.best_eval = test_loss[1]
reward = np.mean(self.env.rewards)
print(reward)
return reward, test_loss[1]
def _eval_data(self):
data_dir = 'data_dir'
if not os.path.exists(data_dir):
os.mkdir(data_dir)
dir_name = datetime.now().strftime("%m-%d-%H-%M-%S")
cur_data_dir = os.path.join(data_dir, dir_name)
os.mkdir(cur_data_dir)
print('===== Saving original graphs ... =====')
with open('%s/original_glist.pkl' % (cur_data_dir, ), 'wb') as f:
original_graphs = [g.to_networkx() for g in self.test_g_list]
cp.dump(original_graphs, f)
print('===== Saved original graphs. =====')
self.env.setup(deepcopy(self.test_g_list))
t = 0
while not self.env.isTerminal():
list_at = self.make_actions(t, greedy=True)
self.env.step(list_at)
t += 1
test_loss = loop_dataset(env.g_list, env.classifier, list(range(len(env.g_list))))
print('\033[93m average test: loss %.5f acc %.5f\033[0m' % (test_loss[0], test_loss[1]))
print('===== Saving new graphs ... =====')
with open('%s/new_glist.pkl' % (cur_data_dir, ), 'wb') as f:
new_graphs = [g.to_networkx() for g in env.g_list]
cp.dump(new_graphs, f)
print('===== Saved new graphs. =====')
return original_graphs, new_graphs, cur_data_dir
def eval(self):
self.env.setup(deepcopy(self.test_g_list))
t = 0
while not self.env.isTerminal():
list_at = self.make_actions(t, greedy=True)
self.env.step(list_at)
t += 1
test_loss = loop_dataset(env.g_list, env.classifier,
list(range(len(env.g_list))))
print('\033[93m average test: loss %.5f acc %.5f\033[0m' %
(test_loss[0], test_loss[1]))
with open('%s/edge_added.txt' % cmd_args.save_dir, 'w') as f:
for i in range(len(self.test_g_list)):
f.write('%d %d ' %
(self.test_g_list[i].label, env.pred[i] + 1))
f.write('%d %d\n' % env.added_edges[i])
reward = np.mean(self.env.rewards)
print(reward)
return reward, test_loss[1]
def eval(self):
self.env.setup(deepcopy(self.test_g_list))
print('===== type of self.test_g_list is\n', type(self.test_g_list))
print('===== length of self.test_g_list is\n', len(self.test_g_list))
print('===== self.test_g_list is\n', self.test_g_list)
print('===== Saving original graphs ...')
# TODO
with open('original_glist.pkl', 'wb') as f:
cp.dump([g.to_networkx() for g in self.test_g_list], f)
print('===== Saved orignal graphs.')
t = 0
while not self.env.isTerminal():
list_at = self.make_actions(t, greedy=True)
self.env.step(list_at)
t += 1
test_loss = loop_dataset(env.g_list, env.classifier, list(range(len(env.g_list))))
print('===== type of env.g_list is\n', type(env.g_list))
print('===== length of env.g_list is\n', len(env.g_list))
print('===== env.g_list is\n', env.g_list)
# TODO
print('===== Saving new graphs ...')
with open('new_glist.pkl', 'wb') as f:
cp.dump([g.to_networkx() for g in env.g_list], f)
print('===== Saved new graphs.')
print('\033[93m average test: loss %.5f acc %.5f\033[0m' % (test_loss[0], test_loss[1]))
if cmd_args.phase == 'train' and self.best_eval is None or test_loss[1] < self.best_eval:
print('----saving to best attacker since this is the best attack rate so far.----')
torch.save(self.net.state_dict(), cmd_args.save_dir + '/epoch-best.model')
with open(cmd_args.save_dir + '/epoch-best.txt', 'w') as f:
f.write('%.4f\n' % test_loss[1])
self.best_eval = test_loss[1]
reward = np.mean(self.env.rewards)
print(reward)
return reward, test_loss[1]
if cmd_args.saved_model is not None and cmd_args.saved_model != '':
print('loading model from %s' % cmd_args.saved_model)
with open('%s-args.pkl' % cmd_args.saved_model, 'rb') as f:
base_args = cp.load(f)
classifier = GraphClassifier(label_map, **vars(base_args))
classifier.load_state_dict(torch.load(cmd_args.saved_model + '.model'))
else:
classifier = GraphClassifier(label_map, **vars(cmd_args))
# move classifier to gpu if available
if cmd_args.ctx == 'gpu':
classifier = classifier.cuda()
# if phase is test look at the test accuracy and loss
if cmd_args.phase == 'test':
test_loss = loop_dataset(test_glist, classifier,
list(range(len(test_glist))))
print('\033[93maverage test: loss %.5f acc %.5f\033[0m' %
(test_loss[0], test_loss[1]))
# if phase is train then fit parameters to the model
if cmd_args.phase == 'train':
optimizer = optim.Adam(classifier.parameters(),
lr=cmd_args.learning_rate)
train_idxes = list(range(len(train_glist)))
best_loss = None
for epoch in range(cmd_args.num_epochs):
random.shuffle(train_idxes)
avg_loss = loop_dataset(train_glist,
classifier,
train_idxes,
def test_graphs(classifier, test_glist):
test_loss = loop_dataset(test_glist, classifier, list(range(len(test_glist))))
print('\033[93maverage test: loss %.5f acc %.5f\033[0m' % (test_loss[0], test_loss[1]))