support subclass of TSDatasetH

qlib/data/dataset/__init__.py

@@ -507,7 +507,9 @@ class TSDatasetH(DatasetH):
         - The dimension of a batch of data <batch_idx, feature, timestep>
     """
 
-    def __init__(self, step_len=30, **kwargs):
+    DEFAULT_STEP_LEN = 30
+
+    def __init__(self, step_len=DEFAULT_STEP_LEN, **kwargs):
         self.step_len = step_len
         super().__init__(**kwargs)
 

qlib/workflow/online/utils.py

@@ -8,8 +8,10 @@ This allows us to use efficient submodels as the market-style changing.
 """
 
 from typing import List, Union
+from qlib.data.dataset import TSDatasetH
 
 from qlib.log import get_module_logger
+from qlib.utils import get_cls_kwargs
 from qlib.workflow.online.update import PredUpdater
 from qlib.workflow.recorder import Recorder
 from qlib.workflow.task.utils import list_recorders
@@ -161,8 +163,9 @@ class OnlineToolR(OnlineTool):
             hist_ref = 0
             task = rec.load_object("task")
             # Special treatment of historical dependencies
-            if task["dataset"]["class"] == "TSDatasetH":
-                hist_ref = task["dataset"]["kwargs"]["step_len"]
+            cls, kwargs = get_cls_kwargs(task["dataset"], default_module="qlib.data.dataset")
+            if issubclass(cls, TSDatasetH):
+                hist_ref = kwargs.get("step_len", TSDatasetH.DEFAULT_STEP_LEN)
             PredUpdater(rec, to_date=to_date, hist_ref=hist_ref).update()
 
         self.logger.info(f"Finished updating {len(online_models)} online model predictions of {self.exp_name}.")