fix minor typos

Author: yule-BUAA

evaluate_link_prediction.py

@@ -169,7 +169,8 @@
                                                                          evaluate_neg_edge_sampler=val_neg_edge_sampler,
                                                                          evaluate_data=val_data,
                                                                          loss_func=loss_func,
-                                                                         num_neighbors=args.num_neighbors)
+                                                                         num_neighbors=args.num_neighbors,
+                                                                         time_gap=args.time_gap)
 
                 new_node_val_losses, new_node_val_metrics = evaluate_model_link_prediction(model_name=args.model_name,
                                                                                            model=model,
@@ -178,7 +179,8 @@
                                                                                            evaluate_neg_edge_sampler=new_node_val_neg_edge_sampler,
                                                                                            evaluate_data=new_node_val_data,
                                                                                            loss_func=loss_func,
-                                                                                           num_neighbors=args.num_neighbors)
+                                                                                           num_neighbors=args.num_neighbors,
+                                                                                           time_gap=args.time_gap)
 
             if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                 # the memory in the best model has seen the validation edges, we need to backup the memory for new testing nodes
@@ -191,7 +193,8 @@
                                                                        evaluate_neg_edge_sampler=test_neg_edge_sampler,
                                                                        evaluate_data=test_data,
                                                                        loss_func=loss_func,
-                                                                       num_neighbors=args.num_neighbors)
+                                                                       num_neighbors=args.num_neighbors,
+                                                                       time_gap=args.time_gap)
 
             if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                 # reload validation memory bank for new testing nodes
@@ -204,7 +207,8 @@
                                                                                          evaluate_neg_edge_sampler=new_node_test_neg_edge_sampler,
                                                                                          evaluate_data=new_node_test_data,
                                                                                          loss_func=loss_func,
-                                                                                         num_neighbors=args.num_neighbors)
+                                                                                         num_neighbors=args.num_neighbors,
+                                                                                         time_gap=args.time_gap)
             # store the evaluation metrics at the current run
             val_metric_dict, new_node_val_metric_dict, test_metric_dict, new_node_test_metric_dict = {}, {}, {}, {}
 

evaluate_node_classification.py

@@ -139,15 +139,17 @@
                                                                              evaluate_idx_data_loader=val_idx_data_loader,
                                                                              evaluate_data=val_data,
                                                                              loss_func=loss_func,
-                                                                             num_neighbors=args.num_neighbors)
+                                                                             num_neighbors=args.num_neighbors,
+                                                                             time_gap=args.time_gap)
 
         test_total_loss, test_metrics = evaluate_model_node_classification(model_name=args.model_name,
                                                                            model=model,
                                                                            neighbor_sampler=full_neighbor_sampler,
                                                                            evaluate_idx_data_loader=test_idx_data_loader,
                                                                            evaluate_data=test_data,
                                                                            loss_func=loss_func,
-                                                                           num_neighbors=args.num_neighbors)
+                                                                           num_neighbors=args.num_neighbors,
+                                                                           time_gap=args.time_gap)
 
         # store the evaluation metrics at the current run
         val_metric_dict, test_metric_dict = {}, {}

train_node_classification.py

@@ -234,7 +234,8 @@
                                                                              evaluate_idx_data_loader=val_idx_data_loader,
                                                                              evaluate_data=val_data,
                                                                              loss_func=loss_func,
-                                                                             num_neighbors=args.num_neighbors)
+                                                                             num_neighbors=args.num_neighbors,
+                                                                             time_gap=args.time_gap)
 
             logger.info(f'Epoch: {epoch + 1}, learning rate: {optimizer.param_groups[0]["lr"]}, train loss: {train_total_loss:.4f}')
             for metric_name in train_metrics.keys():
@@ -255,7 +256,8 @@
                                                                                    evaluate_idx_data_loader=test_idx_data_loader,
                                                                                    evaluate_data=test_data,
                                                                                    loss_func=loss_func,
-                                                                                   num_neighbors=args.num_neighbors)
+                                                                                   num_neighbors=args.num_neighbors,
+                                                                                   time_gap=args.time_gap)
 
                 if args.model_name in ['JODIE', 'DyRep', 'TGN']:
                     # reload validation memory bank for saving models
@@ -289,15 +291,17 @@
                                                                              evaluate_idx_data_loader=val_idx_data_loader,
                                                                              evaluate_data=val_data,
                                                                              loss_func=loss_func,
-                                                                             num_neighbors=args.num_neighbors)
+                                                                             num_neighbors=args.num_neighbors,
+                                                                             time_gap=args.time_gap)
 
         test_total_loss, test_metrics = evaluate_model_node_classification(model_name=args.model_name,
                                                                            model=model,
                                                                            neighbor_sampler=full_neighbor_sampler,
                                                                            evaluate_idx_data_loader=test_idx_data_loader,
                                                                            evaluate_data=test_data,
                                                                            loss_func=loss_func,
-                                                                           num_neighbors=args.num_neighbors)
+                                                                           num_neighbors=args.num_neighbors,
+                                                                           time_gap=args.time_gap)
 
         # store the evaluation metrics at the current run
         val_metric_dict, test_metric_dict = {}, {}