分析代码

main.py

@@ -163,7 +163,7 @@ class LightGBM(object):
         评估模型性能
         :return:
         """
-        fw = open("summary_tag_0321.txt", "a+", encoding="utf-8")
+        fw = open("summary_tag_0319.txt", "a+", encoding="utf-8")
         # 测试数据
         with open("produce_data/x_data_total_return_predict.json") as f1:
             x_list = json.loads(f1.read())

p_data_process.py

@@ -8,7 +8,7 @@ import json
 from functions import generate_hourly_strings
 
 target_string_list = generate_hourly_strings(
-    start_date="2024032000", end_date="2024032023"
+    start_date="2024031900", end_date="2024031923"
 )
 path = "temp_data/data"
 L = []
@@ -20,5 +20,5 @@ for file in target_string_list:
     for obj in data:
         L.append(obj)
 
-with open("prid_data/train_0320.json", "w", encoding="utf-8") as f:
+with open("prid_data/train_0319.json", "w", encoding="utf-8") as f:
     f.write(json.dumps(L, ensure_ascii=False))

process_data.py

@@ -90,8 +90,8 @@ class DataProcessor(object):
             x_path = "data/hour_train.json"
             y_path = "data/daily-label-20240101-20240320.json"
         elif self.flag == "predict":
-            x_path = "prid_data/train_0320.json"
-            y_path = "data/daily-label-20240321-20240325.json"
+            x_path = "prid_data/train_0319.json"
+            y_path = "data/daily-label-20240315-20240321.json"
         else:
             return
         with open(x_path) as f: