labels=labels,
predict_category=args['predict_category'],
device=args['device'],
mode='test')
test_accuracy = get_ogb_evaluator(
predicts=test_y_predicts.argmax(dim=1),
labels=test_y_trues)
print(
f'Epoch: {epoch + 1}, learning rate: {optimizer.param_groups[0]["lr"]}, '
f'train loss: {train_total_loss:.4f}, accuracy {train_accuracy:.4f}, \n'
f'valid loss: {val_total_loss:.4f}, accuracy {val_accuracy:.4f}, \n'
f'test loss: {test_total_loss:.4f}, accuracy {test_accuracy:.4f}')
early_stop = early_stopping.step([('accuracy', val_accuracy, True)], model)
if early_stop:
break
# load best model
early_stopping.load_checkpoint(model)
print('performing model inference...')
# evaluate the best model
model.eval()
nodes_representation = model[0].inference(graph, copy.deepcopy(
{ntype: graph.nodes[ntype].data['feat'] for ntype in graph.ntypes}), device=args['device'])
train_y_predicts = model[1](convert_to_gpu(nodes_representation[args['predict_category']], device=args['device']))[train_idx]
train_y_trues = convert_to_gpu(labels[train_idx], device=args['device'])
device=args['device'],
mode='test')
test_accuracy, test_macro_f1 = evaluate_node_classification(
predicts=test_y_predicts.argmax(dim=1), labels=test_y_trues)
print(
f'Epoch: {epoch + 1}, learning rate: {optimizer.param_groups[0]["lr"]}, train loss: {train_total_loss:.4f}, '
f'accuracy {train_accuracy:.4f}, macro f1 {train_macro_f1:.4f}, \n'
f'valid loss: {val_total_loss:.4f}, '
f'accuracy {val_accuracy:.4f}, macro f1 {val_macro_f1:.4f} \n'
f'test loss: {test_total_loss:.4f}, '
f'accuracy {test_accuracy:.4f}, macro f1 {test_macro_f1:.4f}')
early_stop = early_stopping.step([('accuracy', val_accuracy, True),
('macro_f1', val_macro_f1, True)],
model)
if early_stop:
break
# load best model
early_stopping.load_checkpoint(model)
# evaluate the best model
model.eval()
nodes_representation, _ = model[0].inference(
graph,
copy.deepcopy({(stype, etype, dtype): graph.nodes[dtype].data['feat']
for stype, etype, dtype in graph.canonical_etypes}),
if best_validate_RMSE is None or val_RMSE < best_validate_RMSE:
best_validate_RMSE = val_RMSE
scores = {
"RMSE": float(f"{test_RMSE:.4f}"),
"MAE": float(f"{test_MAE:.4f}")
}
final_result = json.dumps(scores, indent=4)
print(
f'Epoch: {epoch}, learning rate: {optimizer.param_groups[0]["lr"]}, train loss: {train_total_loss:.4f}, RMSE {train_RMSE:.4f}, MAE {train_MAE:.4f}, \n'
f'validate loss: {val_total_loss:.4f}, RMSE {val_RMSE:.4f}, MAE {val_MAE:.4f}, \n'
f'test loss: {test_total_loss:.4f}, RMSE {test_RMSE:.4f}, MAE {test_MAE:.4f}'
)
early_stop = early_stopping.step([('RMSE', val_RMSE, False),
('MAE', val_MAE, False)], model)
if early_stop:
break
# save model result
save_result_folder = f"../results/{args['dataset']}"
if not os.path.exists(save_result_folder):
os.makedirs(save_result_folder, exist_ok=True)
save_result_path = os.path.join(save_result_folder,
f"{args['model_name']}.json")
with open(save_result_path, 'w') as file:
file.write(final_result)
file.close()
