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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]], sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
base_margin_eval_set: Optional[Sequence[_DaskCollection]], base_margin_eval_set: Optional[Sequence[_DaskCollection]],
early_stopping_rounds: Optional[int], early_stopping_rounds: Optional[int],
verbose: bool, verbose: Union[int, bool],
xgb_model: Optional[Union[Booster, XGBModel]], xgb_model: Optional[Union[Booster, XGBModel]],
feature_weights: Optional[_DaskCollection], feature_weights: Optional[_DaskCollection],
callbacks: Optional[Sequence[TrainingCallback]], callbacks: Optional[Sequence[TrainingCallback]],
@ -1797,7 +1797,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None, eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None, eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None, early_stopping_rounds: Optional[int] = None,
verbose: bool = True, verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None, xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None, sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_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]], sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
base_margin_eval_set: Optional[Sequence[_DaskCollection]], base_margin_eval_set: Optional[Sequence[_DaskCollection]],
early_stopping_rounds: Optional[int], early_stopping_rounds: Optional[int],
verbose: bool, verbose: Union[int, bool],
xgb_model: Optional[Union[Booster, XGBModel]], xgb_model: Optional[Union[Booster, XGBModel]],
feature_weights: Optional[_DaskCollection], feature_weights: Optional[_DaskCollection],
callbacks: Optional[Sequence[TrainingCallback]], callbacks: Optional[Sequence[TrainingCallback]],
@ -1906,7 +1906,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None, eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None, eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None, early_stopping_rounds: Optional[int] = None,
verbose: bool = True, verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None, xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None, sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_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_qid: Optional[Sequence[_DaskCollection]],
eval_metric: Optional[Union[str, Sequence[str], Metric]], eval_metric: Optional[Union[str, Sequence[str], Metric]],
early_stopping_rounds: Optional[int], early_stopping_rounds: Optional[int],
verbose: bool, verbose: Union[int, bool],
xgb_model: Optional[Union[XGBModel, Booster]], xgb_model: Optional[Union[XGBModel, Booster]],
feature_weights: Optional[_DaskCollection], feature_weights: Optional[_DaskCollection],
callbacks: Optional[Sequence[TrainingCallback]], callbacks: Optional[Sequence[TrainingCallback]],
@ -2102,7 +2102,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
eval_qid: Optional[Sequence[_DaskCollection]] = None, eval_qid: Optional[Sequence[_DaskCollection]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None, eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: int = None, early_stopping_rounds: int = None,
verbose: bool = False, verbose: Union[int, bool] = False,
xgb_model: Optional[Union[XGBModel, Booster]] = None, xgb_model: Optional[Union[XGBModel, Booster]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None, sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_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_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None, eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None, early_stopping_rounds: Optional[int] = None,
verbose: bool = True, verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None, xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None, sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_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_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Callable]] = None, eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None, early_stopping_rounds: Optional[int] = None,
verbose: bool = True, verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None, xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None, sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None, base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,

24
python-package/xgboost/sklearn.py
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@ -900,7 +900,7 @@ class XGBModel(XGBModelBase):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None, eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None, eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = 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, xgb_model: Optional[Union[Booster, str, "XGBModel"]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None, sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_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 Use `early_stopping_rounds` in :py:meth:`__init__` or
:py:meth:`set_params` instead. :py:meth:`set_params` instead.
verbose : verbose :
If `verbose` and an evaluation set is used, writes the evaluation metric If `verbose` is True and an evaluation set is used, the evaluation metric
measured on the validation set to stderr. 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 : xgb_model :
file name of stored XGBoost model or 'Booster' instance XGBoost model to be file name of stored XGBoost model or 'Booster' instance XGBoost model to be
loaded before training (allows training continuation). loaded before training (allows training continuation).
@ -1362,7 +1365,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None, eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None, eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = 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, xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None, sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_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_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None, eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = 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, xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None, sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_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_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None, eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = 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, xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None, sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_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_qid: Optional[Sequence[ArrayLike]] = None,
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None, eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
early_stopping_rounds: Optional[int] = 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, xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None, sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_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. :py:meth:`set_params` instead.
verbose : verbose :
If `verbose` and an evaluation set is used, writes the evaluation metric If `verbose` is True and an evaluation set is used, the evaluation metric
measured on the validation set to stderr. 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 : xgb_model :
file name of stored XGBoost model or 'Booster' instance XGBoost model to be file name of stored XGBoost model or 'Booster' instance XGBoost model to be
loaded before training (allows training continuation). loaded before training (allows training continuation).