update

qlib/data/dataset/__init__.py

@@ -88,15 +88,8 @@ class DatasetH(Dataset):
         super().__init__(handler, segments)
 
     def init(self, **kwargs):
-
-        logger = get_module_logger("DatasetH")
-        handler_init_kwargs = {}
-        for arg_key, arg_value in kwargs.items():
-            if arg_key in getfullargspec(self.handler.init).args:
-                handler_init_kwargs[arg_key] = arg_value
-            else:
-                logger.info(f"init arguments[{arg_key}] is ignored.")
-        self.handler.init(**handler_init_kwargs)
+        """Initialize the DatasetH, Only parameters belonging to handler.init will be passed in"""
+        self.handler.init(**kwargs)
 
     def setup_data(self, handler: Union[dict, DataHandler], segments: list):
         """

qlib/data/dataset/handler.py

@@ -428,13 +428,11 @@ class DataHandlerLP(DataHandler):
         # TODO: Be able to cache handler data. Save the memory for data processing
 
     def _get_df_by_key(self, data_key: str = DK_I) -> pd.DataFrame:
-        try:
-            df = getattr(self, {self.DK_R: "_data", self.DK_I: "_infer", self.DK_L: "_learn"}[data_key])
-        except AttributeError:
-            print("please set drop_raw = False if you want to use raw data")
-            raise
-        except:
-            raise
+        if data_key == self.DK_R and self.drop_raw:
+            raise AttributeError(
+                "DataHandlerLP has not attribute _data, please set drop_raw = False if you want to use raw data"
+            )
+        df = getattr(self, {self.DK_R: "_data", self.DK_I: "_infer", self.DK_L: "_learn"}[data_key])
         return df
 
     def fetch(