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fixed update bugs

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lzh222333
2021-06-22 08:18:32 +00:00
committed by you-n-g
parent c4c438249c
commit a7862387a2
2 changed files with 9 additions and 4 deletions
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5
qlib/workflow/online/update.py
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@@ -135,10 +135,9 @@ class PredUpdater(RecordUpdater):
# RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU. # RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
# https://github.com/pytorch/pytorch/issues/16797 # https://github.com/pytorch/pytorch/issues/16797
start_time = get_date_by_shift(self.last_end, 1, freq=self.freq) if self.last_end >= self.to_date:
if start_time > self.to_date:
self.logger.info( self.logger.info(
f"The prediction in {self.record.info['id']} are latest ({start_time}). No need to update to {self.to_date}." f"The prediction in {self.record.info['id']} are latest ({self.last_end}). No need to update to {self.to_date}."
) )
return return

8
qlib/workflow/online/utils.py
View File

@@ -189,6 +189,12 @@ class OnlineToolR(OnlineTool):
cls, kwargs = get_cls_kwargs(task["dataset"], default_module="qlib.data.dataset") cls, kwargs = get_cls_kwargs(task["dataset"], default_module="qlib.data.dataset")
if issubclass(cls, TSDatasetH): if issubclass(cls, TSDatasetH):
hist_ref = kwargs.get("step_len", TSDatasetH.DEFAULT_STEP_LEN) hist_ref = kwargs.get("step_len", TSDatasetH.DEFAULT_STEP_LEN)
PredUpdater(rec, to_date=to_date, hist_ref=hist_ref).update() try:
updater = PredUpdater(rec, to_date=to_date, hist_ref=hist_ref)
except OSError:
# skip the recorder without pred
self.logger.warn(f"Can't find `pred.pkl`, skip it.")
continue
updater.update()
self.logger.info(f"Finished updating {len(online_models)} online model predictions of {exp_name}.") self.logger.info(f"Finished updating {len(online_models)} online model predictions of {exp_name}.")