# build the graph for fast query adj_list = [[] for _ in range(max_idx + 1)] for src, dst, eidx, ts in zip(train_src_l, train_dst_l, train_e_idx_l, train_ts_l): adj_list[src].append((dst, eidx, ts)) adj_list[dst].append((src, eidx, ts)) train_ngh_finder = NeighborFinder(adj_list, uniform=UNIFORM) # full graph with all the data for the test and validation purpose full_adj_list = [[] for _ in range(max_idx + 1)] for src, dst, eidx, ts in zip(src_l, dst_l, e_idx_l, ts_l): full_adj_list[src].append((dst, eidx, ts)) full_adj_list[dst].append((src, eidx, ts)) full_ngh_finder = NeighborFinder(full_adj_list, uniform=UNIFORM) train_rand_sampler = RandEdgeSampler(train_src_l, train_dst_l) val_rand_sampler = RandEdgeSampler(src_l, dst_l) test_rand_sampler = RandEdgeSampler(src_l, dst_l) # Model initialize os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" device = torch.device('cuda:{}'.format(GPU)) tgan = TGAN(train_ngh_finder, n_feat, e_feat, n_feat_freeze=args.freeze, num_layers=NUM_LAYER, use_time=USE_TIME, agg_method=AGG_METHOD, attn_mode=ATTN_MODE, seq_len=SEQ_LEN,