format code

qlib/model/ens/ensemble.py

@@ -16,9 +16,9 @@ class Ensemble:
     For example: {Rollinga_b: object, Rollingb_c: object} -> object
 
     When calling this class:
-    
+
         Args:
-            ensemble_dict (dict): the ensemble dict like {name: things} waiting for merging 
+            ensemble_dict (dict): the ensemble dict like {name: things} waiting for merging
 
         Returns:
             object: the ensemble object
@@ -103,6 +103,7 @@ class AverageEnsemble(Ensemble):
         Returns:
             pd.DataFrame: the complete result of averaging and standardizing.
     """
+
     def __call__(self, ensemble_dict: dict) -> pd.DataFrame:
         # need to flatten the nested dict
         ensemble_dict = flatten_dict(ensemble_dict, sep=FLATTEN_TUPLE)

qlib/model/ens/group.py

@@ -64,7 +64,7 @@ class Group:
         else:
             raise NotImplementedError(f"Please specify valid `_ens_func`.")
 
-    def __call__(self, ungrouped_dict: dict, n_jobs:int=1, verbose:int=0, *args, **kwargs) -> dict:
+    def __call__(self, ungrouped_dict: dict, n_jobs: int = 1, verbose: int = 0, *args, **kwargs) -> dict:
         """
         Group the ungrouped_dict into different groups.
 

qlib/model/trainer.py

@@ -240,13 +240,13 @@ class DelayTrainerR(TrainerR):
         """
         Given a list of Recorder and return a list of trained Recorder.
         This class will finish real data loading and model fitting.
- 
+
         Args:
             recs (list): a list of Recorder, the tasks have been saved to them
             end_train_func (Callable, optional): the end_train method which needs at least `recorder`s and `experiment_name`. Defaults to None for using self.end_train_func.
             experiment_name (str): the experiment name, None for use default name.
             kwargs: the params for end_train_func.
- 
+
         Returns:
             List[Recorder]: a list of Recorders
         """