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Update type hints to Python 3.10+ in ignite/metrics/roc_auc.py #3512

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Merged
vfdev-5 merged 1 commit into from
Feb 5, 2026
+6 −6
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Update type hints to Python 3.10+ in ignite/metrics/roc_auc.py #3512

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12 changes: 6 additions & 6 deletions ignite/metrics/roc_auc.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
from typing import Any, Callable, cast, Tuple, Union
from typing import Any, Callable, cast

import torch

Expand All @@ -15,7 +15,7 @@ def roc_auc_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:
return roc_auc_score(y_true, y_pred)


def roc_auc_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> Tuple[Any, Any, Any]:
def roc_auc_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> tuple[Any, Any, Any]:
from sklearn.metrics import roc_curve

y_true = y_targets.cpu().numpy()
Expand Down Expand Up @@ -83,7 +83,7 @@ def __init__(
self,
output_transform: Callable = lambda x: x,
check_compute_fn: bool = False,
device: Union[str, torch.device] = torch.device("cpu"),
device: str | torch.device = torch.device("cpu"),
skip_unrolling: bool = False,
):
try:
Expand Down Expand Up @@ -166,7 +166,7 @@ def __init__(
self,
output_transform: Callable = lambda x: x,
check_compute_fn: bool = False,
device: Union[str, torch.device] = torch.device("cpu"),
device: str | torch.device = torch.device("cpu"),
skip_unrolling: bool = False,
) -> None:
try:
Expand All @@ -182,7 +182,7 @@ def __init__(
skip_unrolling=skip_unrolling,
)

def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: ignore[override]
def compute(self) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: ignore[override]
if len(self._predictions) < 1 or len(self._targets) < 1:
raise NotComputableError("RocCurve must have at least one example before it can be computed.")

Expand All @@ -197,7 +197,7 @@ def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # type: i

if idist.get_rank() == 0:
# Run compute_fn on zero rank only
fpr, tpr, thresholds = cast(Tuple, self.compute_fn(_prediction_tensor, _target_tensor))
fpr, tpr, thresholds = cast(tuple, self.compute_fn(_prediction_tensor, _target_tensor))
fpr = torch.tensor(fpr, dtype=self._double_dtype, device=_prediction_tensor.device)
tpr = torch.tensor(tpr, dtype=self._double_dtype, device=_prediction_tensor.device)
thresholds = torch.tensor(thresholds, dtype=self._double_dtype, device=_prediction_tensor.device)
Expand Down
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