聚烯烃日度调试

config_juxiting.py

@@ -257,14 +257,17 @@ ClassifyId = 1161
 
 # # 八大维度数据项编码
 # bdwd_items = {
-#     'ciri': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE',
+#     'ciri': '251889263|FORECAST|PRICE|T01',
-#     'benzhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE01',
+#     'cierri': '251889263|FORECAST|PRICE|T02',
-#     'cizhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE02',
+#     'cisanri': '251889263|FORECAST|PRICE|T03',
-#     'gezhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE03',
+#     'cisiri': '251889263|FORECAST|PRICE|T04',
-#     'ciyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE04',
+#     'benzhou': '251889263|FORECAST|PRICE|T05',
-#     'cieryue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE05',
+#     'cizhou': '251889263|FORECAST|PRICE|W_01',
-#     'cisanyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE06',
+#     'gezhou': '251889263|FORECAST|PRICE|W_02',
-#     'cisiyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE07',
+#     'ciyue': '251889263|FORECAST|PRICE|M_01',
+#     'cieryue': '251889263|FORECAST|PRICE|M_02',
+#     'cisanyue': '251889263|FORECAST|PRICE|M_03',
+#     'cisiyue': '251889263|FORECAST|PRICE|M_04',
 # }
 
 
@@ -440,9 +443,9 @@ is_timefurture = True  # 是否使用时间特征
 is_fivemodels = False  # 是否使用之前保存的最佳的5个模型
 is_edbcode = False  # 特征使用edbcoding列表中的
 is_edbnamelist = False  # 自定义特征，对应上面的edbnamelist
-is_update_eta = False  # 预测结果上传到eta
+is_update_eta = True  # 预测结果上传到eta
 is_update_report = False  # 是否上传报告
-is_update_warning_data = False  # 是否上传预警数据
+is_update_warning_data = True  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
 is_del_tow_month = True  # 是否删除两个月不更新的特征

main_juxiting.py

@@ -521,8 +521,17 @@ def predict_main():
 
     # logger.info('制作报告end')
 
-    push_market_value()
+    try:
-    sql_inset_predict(global_config)
+        push_market_value()
+        logger.info('推送市场值完成')
+    except Exception as e:
+        logger.info(f'推送市场值失败：{e}')
+
+    try:
+        sql_inset_predict(global_config)
+        logger.info('插入预测数据完成')
+    except Exception as e:
+        logger.info(f'插入预测数据失败：{e}')
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
@@ -549,16 +558,16 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    # for i_time in pd.date_range('2025-6-2', '2025-7-23', freq='B'):
+    for i_time in pd.date_range('2025-8-1', '2025-8-11', freq='B'):
-    #     try:
+        try:
-    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #         global_config['db_mysql'].connect()
+            global_config['db_mysql'].connect()
-    #         predict_main()
+            predict_main()
-    #     except Exception as e:
+        except Exception as e:
-    #         logger.info(f'预测失败：{e}')
+            logger.info(f'预测失败：{e}')
-    #         continue
+            continue
     # global_config['end_time'] = '2025-08-05'
-    predict_main()
+    # predict_main()
 
     # global_config['end_time'] = '2025-08-01'
     # push_market_value()

main_juxiting_yuedu.py

@@ -552,12 +552,12 @@ if __name__ == '__main__':
     #         continue
 
     # global_config['end_time'] = '2025-07-25'
-    predict_main()
+    # predict_main()
     # push_market_value()
     # sql_inset_predict(global_config)
 
     #  图片报告
-    # global_config['end_time'] = '2025-08-05'
+    global_config['end_time'] = '2025-08-12'
-    # logger.info('图片报告ing')
+    logger.info('图片报告ing')
-    # pp_bdwd_png(global_config=global_config)
+    pp_bdwd_png(global_config=global_config)
-    # logger.info('图片报告end')
+    logger.info('图片报告end')

models/nerulforcastmodels.py

@@ -321,6 +321,8 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
     df['ds'] = pd.to_datetime(df['ds'], errors='coerce')
     # df 数值列转为 float32
     for col in df.select_dtypes(include=['int']).columns:
+        if col == 'ds':
+            continue
         df[col] = df[col].astype(np.float32)
 
     # 设置中文字体
@@ -351,8 +353,12 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
 
     # 特征重要度
     X_train = df_train.drop(columns=['y', 'ds'])
-    if 'yearmonthweeks' in df_train.columns:
+    if 'yearmonthweeks' in X_train.columns:
-        X_train = df_train.drop(columns=['yearmonthweeks'])
+        X_train = X_train.drop(columns=['yearmonthweeks'])  
+    # 自动检测并删除所有datetime类型列
+    datetime_cols = X_train.select_dtypes(include=['datetime64']).columns
+    if not datetime_cols.empty:
+        X_train = X_train.drop(columns=datetime_cols)
 
     y_train = df_train['y']
     feature_importance(X_train=X_train, y_train=y_train)