updawte ds docs and delay trainer bug

qlib/data/dataset/weight.py

@@ -56,6 +56,7 @@ def __len__(self):
 class SampleReweighter(Reweighter):
     """
     The sample-wise reweighter. It aims to reweight by the given weight of each sample.
+    The samples are indexed in a pandas way
     """
 
     def __init__(self, sample_weights: pd.Series, *args, **kwargs):
@@ -82,7 +83,7 @@ def _sample_reweight_DataFrame(self, samples: Union[pd.Series, pd.DataFrame], *a
         weight.update(self.weights)
         return weight
 
-    def _sample_reweight_TSDataSampler(self, sampler: TSDataSampler, *args, **kwargs):
+    def _sample_reweight_TSDataSampler(self, sampler: TSDataSampler, *args, **kwargs) -> WeightSampler:
         """
         This function processes the prepared data with TSDataSampler type.
 

qlib/model/meta/model.py

@@ -10,7 +10,11 @@
 
 class MetaModel(metaclass=abc.ABCMeta):
     """
-    The meta-model controls the training process.
+    The meta-model guiding the model learning.
+
+    The word `Guiding` can be categorized into two types based on the stage of model learning
+    - The definition of learning tasks:  Please refer to docs of `MetaTaskModel`
+    - Controlling the learning process of models: Please refer to the docs of `MetaGuideModel`
     """
 
     @abc.abstractmethod
@@ -21,9 +25,14 @@ def fit(self, *args, **kwargs):
         pass
 
     @abc.abstractmethod
-    def inference(self, *args, **kwargs):
+    def inference(self, *args, **kwargs) -> object:
         """
         The inference process of the meta-model.
+
+        Returns
+        -------
+        object:
+            Some information to guide the model learning
         """
         pass
 
@@ -38,6 +47,9 @@ def prepare_task(self, task: MetaTask) -> dict:
         """
         Input a meta task and output a task with qlib format
 
+        When modifying the model tasks, the meta model will leverage `self.inference` to get some necessary
+        information.
+
         Parameters
         ----------
         task : MetaTask

qlib/model/trainer.py

@@ -497,13 +497,17 @@ def train(self, tasks: list, train_func=None, experiment_name: str = None, **kwa
             tasks = [tasks]
         if len(tasks) == 0:
             return []
-        return super().train(
+        _skip_run_task = self.skip_run_task
+        self.skip_run_task = False  # The task preparation can't be skipped
+        res = super().train(
             tasks,
             train_func=train_func,
             experiment_name=experiment_name,
             after_status=TaskManager.STATUS_PART_DONE,
             **kwargs,
         )
+        self.skip_run_task = _skip_run_task
+        return res
 
     def end_train(self, recs, end_train_func=None, experiment_name: str = None, **kwargs) -> List[Recorder]:
         """