cws

杭州日电量/industry_elec_cws.py

@@ -43,6 +43,7 @@ def to_data(file_dir, excel):
     data.drop(columns=[i for i in data.columns if (data[i] == 0).sum() / len(data) >= 0.5], inplace=True)  # 去除0值列
     print('len(data):', len(data))
     for industry in data.columns[1:]:
+        c = time.time()
         df = data[['stat_date', industry]]
         df = df[df[industry] != 0]  # 去除0值行
 
@@ -58,7 +59,7 @@ 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("========")
         print('len(dataset_x:)', len(dataset_x))
 
         # 划分训练集和测试集
@@ -82,7 +83,9 @@ def to_data(file_dir, excel):
         train_loss = []
         loss_function = nn.MSELoss()
         optimizer = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
+
         a = time.time()
+        print("行业加载时间", a-c)
         for i in range(1200):
             out = model(train_x)
             loss = loss_function(out, train_y)
@@ -126,6 +129,7 @@ def to_data(file_dir, excel):
         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")
+        print("========")
 
 
 if __name__ == '__main__':