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Update black formatter

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Jactus
2020-11-11 09:34:10 +08:00
parent 1a8ef55dc7
commit 722655ad13
15 changed files with 111 additions and 114 deletions
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2
qlib/utils/__init__.py
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@@ -195,7 +195,7 @@ def get_cls_kwargs(config: Union[dict, str], module) -> (type, dict):
def init_instance_by_config(
config: Union[str, dict, object], module=None, accept_types: Union[type, Tuple[type]] = tuple([]), **kwargs
config: Union[str, dict, object], module=None, accept_types: Union[type, Tuple[type]] = tuple([]), **kwargs
) -> object:
"""
get initialized instance with config

10
qlib/utils/paral.py
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@@ -5,8 +5,8 @@ from joblib import Parallel, delayed
import pandas as pd
def datetime_groupby_apply(df, apply_func, axis=0, level='datetime', resample_rule="M", n_jobs=-1, skip_group=False):
""" datetime_groupby_apply
def datetime_groupby_apply(df, apply_func, axis=0, level="datetime", resample_rule="M", n_jobs=-1, skip_group=False):
"""datetime_groupby_apply
This function will apply the `apply_func` on the datetime level index.
Parameters
@@ -26,12 +26,14 @@ def datetime_groupby_apply(df, apply_func, axis=0, level='datetime', resample_ru
Returns:
pd.DataFrame
"""
def _naive_group_apply(df):
return df.groupby(axis=axis, level=level).apply(apply_func)
if n_jobs != 1:
dfs = Parallel(n_jobs=n_jobs)(delayed(_naive_group_apply)(sub_df)
for idx, sub_df in df.resample(resample_rule, axis=axis, level=level))
dfs = Parallel(n_jobs=n_jobs)(
delayed(_naive_group_apply)(sub_df) for idx, sub_df in df.resample(resample_rule, axis=axis, level=level)
)
return pd.concat(dfs, axis=axis).sort_index()
else:
return _naive_group_apply(df)