cws

杭州日电量/industry_elec_cws.py

@@ -58,6 +58,7 @@ def to_data(file_dir, excel):
         df = (df - min_value) / (max_value - min_value)
 
         dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
+        print()
         print('len(dataset_x:)', len(dataset_x))
 
         # 划分训练集和测试集
@@ -91,7 +92,7 @@ def to_data(file_dir, excel):
             train_loss.append(loss.item())
         b = time.time()
 
-        print(excel,industry,'训练用时',b-a)
+        print(excel, industry, '训练用时', b - a)
 
         # 保存模型
         # torch.save(model.state_dict(),save_filename)
@@ -121,7 +122,7 @@ def to_data(file_dir, excel):
         result_eight = pd.DataFrame({'pred_test': pred_test[-31:], 'real': df[-31:]})
         target = (result_eight['pred_test'][-3:].sum() - result_eight['real'][-3:].sum()) / result_eight[
             'real'].sum()
-        print(target)
+        # print(target)
         with open(fr'.\cws_to_data\{excel[:2]}.txt', 'a', encoding='utf-8') as f:
             tmp_data = {'city': excel[:2], 'industry': industry, "month_deviation_rate": round(target, 5)}
             f.write(str(tmp_data) + "\n")