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Fix incomplete type hints for verbose (#7945)

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Tim Sabsch 2022-05-30 06:08:24 +02:00 committed by GitHub
parent fbc3d861bb
commit 7a039e03fe
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2 changed files with 23 additions and 17 deletions
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16
python-package/xgboost/dask.py
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@ -1731,7 +1731,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
early_stopping_rounds: Optional[int],
verbose: bool,
verbose: Union[int, bool],
xgb_model: Optional[Union[Booster, XGBModel]],
feature_weights: Optional[_DaskCollection],
callbacks: Optional[Sequence[TrainingCallback]],
@ -1797,7 +1797,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: bool = True,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
@ -1826,7 +1826,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
early_stopping_rounds: Optional[int],
verbose: bool,
verbose: Union[int, bool],
xgb_model: Optional[Union[Booster, XGBModel]],
feature_weights: Optional[_DaskCollection],
callbacks: Optional[Sequence[TrainingCallback]],
@ -1906,7 +1906,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: bool = True,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
@ -2027,7 +2027,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
eval_qid: Optional[Sequence[_DaskCollection]],
eval_metric: Optional[Union[str, Sequence[str], Metric]],
early_stopping_rounds: Optional[int],
verbose: bool,
verbose: Union[int, bool],
xgb_model: Optional[Union[XGBModel, Booster]],
feature_weights: Optional[_DaskCollection],
callbacks: Optional[Sequence[TrainingCallback]],
@ -2102,7 +2102,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
eval_qid: Optional[Sequence[_DaskCollection]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: int = None,
verbose: bool = False,
verbose: Union[int, bool] = False,
xgb_model: Optional[Union[XGBModel, Booster]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
@ -2167,7 +2167,7 @@ class DaskXGBRFRegressor(DaskXGBRegressor):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: bool = True,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
@ -2231,7 +2231,7 @@ class DaskXGBRFClassifier(DaskXGBClassifier):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: bool = True,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,

24
python-package/xgboost/sklearn.py
View File

@ -900,7 +900,7 @@ class XGBModel(XGBModelBase):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: Optional[bool] = True,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, "XGBModel"]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
@ -938,8 +938,11 @@ class XGBModel(XGBModelBase):
Use `early_stopping_rounds` in :py:meth:`__init__` or
:py:meth:`set_params` instead.
verbose :
If `verbose` and an evaluation set is used, writes the evaluation metric
measured on the validation set to stderr.
If `verbose` is True and an evaluation set is used, the evaluation metric
measured on the validation set is printed to stdout at each boosting stage.
If `verbose` is an integer, the evaluation metric is printed at each `verbose`
boosting stage. The last boosting stage / the boosting stage found by using
`early_stopping_rounds` is also printed.
xgb_model :
file name of stored XGBoost model or 'Booster' instance XGBoost model to be
loaded before training (allows training continuation).
@ -1362,7 +1365,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: Optional[bool] = True,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
@ -1604,7 +1607,7 @@ class XGBRFClassifier(XGBClassifier):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: Optional[bool] = True,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
@ -1676,7 +1679,7 @@ class XGBRFRegressor(XGBRegressor):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: Optional[bool] = True,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
@ -1755,7 +1758,7 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
eval_qid: Optional[Sequence[ArrayLike]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = None,
verbose: Optional[bool] = False,
verbose: Optional[Union[bool, int]] = False,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
@ -1814,8 +1817,11 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
:py:meth:`set_params` instead.
verbose :
If `verbose` and an evaluation set is used, writes the evaluation metric
measured on the validation set to stderr.
If `verbose` is True and an evaluation set is used, the evaluation metric
measured on the validation set is printed to stdout at each boosting stage.
If `verbose` is an integer, the evaluation metric is printed at each `verbose`
boosting stage. The last boosting stage / the boosting stage found by using
`early_stopping_rounds` is also printed.
xgb_model :
file name of stored XGBoost model or 'Booster' instance XGBoost model to be
loaded before training (allows training continuation).