add ddi_graph to eval_model

predictor/cross_validation.py

 
  
 def eval_model(model, optimizer, loss_func, train_data, test_data,
-               batch_size, n_epoch, patience, gpu_id, mdl_dir):
+               batch_size, n_epoch, patience, ddi_graph, gpu_id, mdl_dir):
     tr_indices, es_indices = random_split_indices(len(train_data), test_rate=0.1)
     train_loader = FastTensorDataLoader(*train_data.tensor_samples(tr_indices), batch_size=batch_size, shuffle=True)
     valid_loader = FastTensorDataLoader(*train_data.tensor_samples(es_indices), batch_size=len(es_indices) // 4)
     test_loader = FastTensorDataLoader(*test_data.tensor_samples(), batch_size=len(test_data) // 4)
-    train_model(model, optimizer, loss_func, train_loader, valid_loader, n_epoch, patience, gpu_id,
+    train_model(model, optimizer, loss_func, train_loader, valid_loader, n_epoch, patience, gpu_id, ddi_graph,
                 sl=True, mdl_dir=mdl_dir)
-    test_loss = eval_epoch(model, test_loader, loss_func, gpu_id)
+    test_loss = eval_epoch(model, test_loader, loss_func, ddi_graph, gpu_id)
     test_loss /= len(test_data)
     return test_loss
 
         if not os.path.exists(test_mdl_dir):
             os.makedirs(test_mdl_dir)
         test_loss = eval_model(model, optimizer, loss_func, train_data, test_data,
-                               args.batch, args.epoch, args.patience, gpu_id, test_mdl_dir)
+                               args.batch, args.epoch, args.patience, ddi_graph, gpu_id, test_mdl_dir)
         test_losses.append(test_loss)
         logging.info("Test loss: {:.4f}".format(test_loss))
         logging.info("*" * n_delimiter + '\n')