pos_label = torch.ones(size, dtype=torch.float, device=device)
neg_label = torch.zeros(size, dtype=torch.float, device=device)
optimizer.zero_grad()
tgan = tgan.train()
pos_prob, neg_prob = tgan.contrast(src_l_cut, dst_l_cut, dst_l_fake,
ts_l_cut, NUM_NEIGHBORS)
loss = criterion(pos_prob, pos_label)
loss += criterion(neg_prob, neg_label)
loss.backward()
optimizer.step()
# get training results
with torch.no_grad():
tgan = tgan.eval()
pred_score = np.concatenate([(pos_prob).cpu().detach().numpy(),
(neg_prob).cpu().detach().numpy()])
pred_label = pred_score > 0.5
true_label = np.concatenate([np.ones(size), np.zeros(size)])
acc = accuracy_score(true_label, pred_label)
ap = average_precision_score(true_label, pred_label)
f1 = f1_score(true_label, pred_label)
auc = roc_auc_score(true_label, pred_score)
batch_bar.set_postfix(loss=loss.item(), acc=acc, f1=f1, auc=auc)
# validation phase use all information
tgan.ngh_finder = full_ngh_finder
val_acc, val_ap, val_f1, val_auc = eval_one_epoch('val for old nodes',
tgan, val_src_l,
val_dst_l, val_ts_l,
full_adj_list[dst].append((src, eidx, ts))
full_ngh_finder = NeighborFinder(full_adj_list, uniform=UNIFORM)
### Model initialize
device = torch.device('cuda:{}'.format(GPU))
tgan = TGAN(full_ngh_finder, n_feat, e_feat,
num_layers=NUM_LAYER, use_time=USE_TIME, agg_method=AGG_METHOD, attn_mode=ATTN_MODE,
seq_len=SEQ_LEN, n_head=NUM_HEADS, drop_out=DROP_OUT, node_dim=NODE_DIM, time_dim=TIME_DIM)
# optimizer = torch.optim.Adam(tgan.parameters(), lr=LEARNING_RATE)
# criterion = torch.nn.BCELoss()
tgan = tgan.to(device)
#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('TGAN starts generating representations')
with torch.no_grad():
tgan.eval()
src_embed = tgan.tem_conv(src_l, ts_l, NODE_LAYER)
tgt_embed = tgan.tem_conv(dst_l, ts_l, NODE_LAYER)
g_df['user_embed'] = [emb[0] for emb in src_embed.tolist()]
g_df['tgt_embed'] = [emb[0] for emb in tgt_embed.tolist()]
inv_i_dict = {v: k for k, v in i_dict.items()}
tgtpg_item = [inv_i_dict.get(i) for i in target_pages]
tgt_emb = [g_df.loc[orig_df['item'] == item, 'tgt_embed'].iloc[0] for item in tgtpg_item]
g_df = g_df.groupby('u').tail(1)