params = f"batchsize={BATCH_SIZE},layers={NUM_LAYER},neighbors={NUM_NEIGHBORS},uniform={UNIFORM}"
with open(res_path, "a") as file:
file.write("tgat,{},{:.4f},{:.4f},\"{}\"".format(DATA, time_batch,
time_epoch, params))
file.write("\n")
exit(0)
np.random.shuffle(idx_list)
early_stopper = EarlyStopMonitor()
epoch_bar = trange(NUM_EPOCH)
for epoch in epoch_bar:
# Training
# training use only training graph
tgan.ngh_finder = train_ngh_finder
np.random.shuffle(idx_list)
batch_bar = trange(num_batch)
for k in batch_bar:
s_idx = k * BATCH_SIZE
e_idx = min(num_instance - 1, s_idx + BATCH_SIZE)
src_l_cut = train_src_l[s_idx:e_idx]
dst_l_cut = train_dst_l[s_idx:e_idx]
ts_l_cut = train_ts_l[s_idx:e_idx]
size = len(src_l_cut)
src_l_fake, dst_l_fake = train_rand_sampler.sample(size)
with torch.no_grad():
pos_label = torch.ones(size, dtype=torch.float, device=device)
neg_label = torch.zeros(size, dtype=torch.float, device=device)
logger.debug('num of training instances: {}'.format(num_instance))
logger.debug('num of batches per epoch: {}'.format(num_batch))
idx_list = np.arange(num_instance)
np.random.shuffle(idx_list)
logger.info('loading saved TGAN model')
model_path = f'./saved_models/{args.prefix}-{args.agg_method}-{args.attn_mode}-{DATA}.pth'
tgan.load_state_dict(torch.load(model_path))
tgan.eval()
logger.info('TGAN models loaded')
logger.info('Start training node classification task')
lr_model = LR(n_feat.shape[1])
lr_optimizer = torch.optim.Adam(lr_model.parameters(), lr=args.lr)
######################## lr_model = lr_model.to(device)
tgan.ngh_finder = full_ngh_finder
idx_list = np.arange(len(train_src_l))
lr_criterion = torch.nn.BCELoss()
lr_criterion_eval = torch.nn.BCELoss()
def eval_epoch(src_l, dst_l, ts_l, label_l, batch_size, lr_model, tgan, num_layer=NODE_LAYER):
pred_prob = np.zeros(len(src_l))
loss = 0
num_instance = len(src_l)
num_batch = math.ceil(num_instance / batch_size)
with torch.no_grad():
lr_model.eval()
tgan.eval()
for k in range(num_batch):
s_idx = k * batch_size
e_idx = min(num_instance - 1, s_idx + batch_size)