def test(config):
device = torch.device(config.device)
# load data
data_dir = os.path.join(config.model, config.dataset)
test_fname = os.path.join(data_dir, config.test_data)
test_data = get_loader(test_fname, config.batch)
wordemb = np.loadtxt(os.path.join(data_dir, config.wordmat_file))
# charemb = np.loadtxt(os.path.join(data_dir, config.charmat_file))
# init model
model = GCM(dim_word=config.dim_word,
num_channel=config.num_channel,
kernel_size=config.kernel_size,
aspect_kernel_size=config.aspect_kernel_size,
num_layer=config.num_layer,
num_class=config.num_class,
wordmat=wordemb,
dropout_rate=config.dropout_rate,
device=device)
# load model
model_save_dir = os.path.join(config.model_save, config.dataset,
config.model)
result_dir = os.path.join(config.result_save, config.dataset, config.model,
'test')
if not os.path.exists(result_dir):
os.makedirs(result_dir)
save_fout = open(os.path.join(result_dir, 'best.txt'), 'w')
model.load_state_dict(torch.load(os.path.join(model_save_dir, 'best.pth')))
model = model.to(device)
model.eval()
# init loss
logit_list = []
rating_list = []
for batch_data in tqdm(test_data):
sent_ids, lens, aspect_ids, aspect_lens, polarity, pws = batch_data
sent_ids, aspect_ids, polarity = sent_ids.to(device), aspect_ids.to(
device), polarity.to(device)
logit = model(sent_ids, aspect_ids)
save(sent_ids.tolist(), lens.tolist(), aspect_ids.tolist(),
aspect_lens.tolist(), polarity.tolist(), logit.tolist(),
save_fout, config)
logit_list.append(logit.cpu().data.numpy())
rating_list.append(polarity.cpu().data.numpy())
test_acc, test_precision, test_recall, test_f1 = get_score(
np.concatenate(logit_list, 0), np.concatenate(rating_list, 0))
print(
'test_acc=%.4f, test_precision=%.4f, test_recall=%.4f, test_f1=%.4f' %
(test_acc, test_precision, test_recall, test_f1))
