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chore(deps): update pre-commit hook psf/black to v24 - abandoned #137

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3e1b719
chore(deps): update pre-commit hook psf/black to v24
renovate[bot] Jan 26, 2024
388dbfe
chore(pre-commit.ci): auto fixes
pre-commit-ci[bot] Jan 26, 2024
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2 changes: 1 addition & 1 deletion .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@ repos:
hooks:
- id: isort
- repo: https://github.com/psf/black
rev: 23.12.1
rev: 24.1.0
hooks:
- id: black
- repo: https://github.com/codespell-project/codespell
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2 changes: 1 addition & 1 deletion src/boost_loss/base.py
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Expand Up @@ -343,7 +343,7 @@ def eval_metric_lgb(
self,
y_true: NDArray | lgb.Dataset | xgb.DMatrix,
y_pred: NDArray | lgb.Dataset | xgb.DMatrix,
sample_weight: NDArray | lgb.Dataset | xgb.DMatrix | None = None
sample_weight: NDArray | lgb.Dataset | xgb.DMatrix | None = None,
# not used, exists for eval_metric_xgb_sklearn
) -> tuple[str, float, bool]:
"""LightGBM-compatible interface"""
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4 changes: 1 addition & 3 deletions src/boost_loss/regression/regression.py
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Expand Up @@ -204,9 +204,7 @@ def grad(self, y_true: NDArray, y_pred: NDArray) -> NDArray:
return -y_true / (1 + np.exp(y_true * y_pred))

def hess(self, y_true: NDArray, y_pred: NDArray) -> NDArray:
return (
y_true**2 * np.exp(y_true * y_pred) / (1 + np.exp(y_true * y_pred)) ** 2
)
return y_true**2 * np.exp(y_true * y_pred) / (1 + np.exp(y_true * y_pred)) ** 2


class MSLELoss(LossBase):
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31 changes: 18 additions & 13 deletions src/boost_loss/regression/sklearn.py
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Expand Up @@ -217,32 +217,37 @@ def predict_raw(self, X: Any, **predict_params: Any) -> NDArray[Any]:
def predict(
self,
X: Any,
type_: Literal["mean", "median", "var", "std", "range", "mae", "mse"]
| None = None,
type_: (
Literal["mean", "median", "var", "std", "range", "mae", "mse"] | None
) = None,
return_std: Literal[False] = False,
**predict_params: Any,
) -> NDArray[Any]:
...
) -> NDArray[Any]: ...

@overload
def predict(
self,
X: Any,
type_: tuple[
Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]
]
| None = None,
type_: (
tuple[
Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]
]
| None
) = None,
return_std: Literal[True] = ...,
**predict_params: Any,
) -> tuple[NDArray[Any], NDArray[Any]]:
...
) -> tuple[NDArray[Any], NDArray[Any]]: ...

def predict(
self,
X: Any,
type_: Literal["mean", "median", "var", "std", "range", "mae", "mse"]
| tuple[Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]]
| None = None,
type_: (
Literal["mean", "median", "var", "std", "range", "mae", "mse"]
| tuple[
Literal["mean", "median"], Literal["var", "std", "range", "mae", "mse"]
]
| None
) = None,
return_std: bool = False,
**predict_params: Any,
) -> NDArray[Any] | tuple[NDArray[Any], NDArray[Any]]:
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6 changes: 2 additions & 4 deletions src/boost_loss/sklearn.py
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Expand Up @@ -30,8 +30,7 @@ def apply_custom_loss(
target_transformer: None = ...,
recursive: bool = ...,
recursive_strict: bool = ...,
) -> TEstimator:
...
) -> TEstimator: ...


@overload
Expand All @@ -46,8 +45,7 @@ def apply_custom_loss(
target_transformer: BaseEstimator = ...,
recursive: bool = ...,
recursive_strict: bool = ...,
) -> TransformedTargetRegressor:
...
) -> TransformedTargetRegressor: ...


def apply_custom_loss(
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