def test(**kwargs):
if 'dataset' not in kwargs:
opt = getattr(config, 'AmazonDigitalMusic_Config')()
else:
opt = getattr(config, kwargs['dataset'] + '_Config')()
opt.parse(kwargs)
assert(len(opt.pth_path) > 0)
random.seed(opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.use_gpu:
torch.cuda.manual_seed_all(opt.seed)
if len(opt.gpu_ids) == 0 and opt.use_gpu:
torch.cuda.set_device(opt.gpu_id)
model = Model(opt, getattr(models, opt.model))
if opt.use_gpu:
model.cuda()
if len(opt.gpu_ids) > 0:
model = nn.DataParallel(model, device_ids=opt.gpu_ids)
if model.net.num_fea != opt.num_fea:
raise ValueError(f"the num_fea of {opt.model} is error, please specific --num_fea={model.net.num_fea}")
model.load(opt.pth_path)
print(f"load model: {opt.pth_path}")
test_data = ReviewData(opt.data_root, mode="Test")
test_data_loader = DataLoader(test_data, batch_size=opt.batch_size, shuffle=False, collate_fn=collate_fn)
print(f"{now()}: test in the test datset")
predict_loss, test_mse, test_mae = predict(model, test_data_loader, opt)
def test(**kwargs):
if 'dataset' not in kwargs:
opt = getattr(config, 'Gourmet_Food_data_Config')()
else:
opt = getattr(config, kwargs['dataset'] + '_Config')()
opt.parse(kwargs)
logging.basicConfig(
filename=f"logs/{opt}.log",
filemode="w",
format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
datefmt="%d-%m-%Y %H:%M:%S",
level=logging.DEBUG)
random.seed(opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.use_gpu:
torch.cuda.manual_seed_all(opt.seed)
if len(opt.gpu_ids) == 0 and opt.use_gpu:
torch.cuda.set_device(opt.gpu_id)
model = Model(opt, getattr(models, opt.model)).cuda()
print("load...")
model.load(
"./checkpoints/DPHP_Gourmet_Food_data_cfg-Gourmet_Food_data-poolatt-lr0.001-wd0.0005-drop0.1-id32-hidden100.pth"
)
test_data = ReviewData(opt.data_root, mode="Test")
test_data_loader = DataLoader(test_data,
batch_size=opt.batch_size,
shuffle=False,
collate_fn=collate_fn)
auc, corr, predict_loss = predict(model, test_data_loader, opt, logging)
def generate_conditional_sentence(**kwargs):
if 'dataset' not in kwargs:
opt = getattr(config, 'AmazonDigitalMusic_Config')()
else:
opt = getattr(config, kwargs['dataset'] + '_Config')()
opt.parse(kwargs)
assert(len(opt.pth_path) > 0)
random.seed(opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.use_gpu:
torch.cuda.manual_seed_all(opt.seed)
if len(opt.gpu_ids) == 0 and opt.use_gpu:
torch.cuda.set_device(opt.gpu_id)
model = Model(opt, getattr(models, opt.model))
if opt.use_gpu:
model.cuda()
if len(opt.gpu_ids) > 0:
model = nn.DataParallel(model, device_ids=opt.gpu_ids)
if model.net.num_fea != opt.num_fea:
raise ValueError(f"the num_fea of {opt.model} is error, please specific --num_fea={model.net.num_fea}")
model.load(opt.pth_path)
print(f"load model: {opt.pth_path}")
test_data = ReviewData(opt.data_root, mode="Test")
test_data_loader = DataLoader(test_data, batch_size=1, shuffle=False, collate_fn=collate_fn)
print(f"{now()}: generating conditional sentence...")
model.eval()
with torch.no_grad():
user_review_dict = np.load("./dataset/AmazonDigitalMusic/train/plainUserReviews.npy", allow_pickle=True).item()
item_review_dict = np.load("./dataset/AmazonDigitalMusic/train/plainItemReviews.npy", allow_pickle=True).item()
cnt = 10
for idx, (test_input, scores) in enumerate(test_data_loader):
if idx == cnt:
test_input = unpack_input(opt, test_input)
output = model(test_input, mode="Generate")
uid = test_input[2].item()
user_reviews = user_review_dict[uid]
iid = test_input[3].item()
item_reviews = item_review_dict[iid]
imp_user_review_id = output[0].cpu().numpy().squeeze()
imp_user_review_id = np.argmax(imp_user_review_id)
print(user_reviews[imp_user_review_id])
imp_item_review_id = output[1].cpu().numpy().squeeze()
imp_item_review_id = np.argmax(imp_item_review_id)
print(item_reviews[imp_item_review_id])
break
