def multiclass_auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
num_classes: Optional[int] = None,
) -> torch.Tensor:
"""
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from multiclass
prediction scores
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.auroc.auroc`. Will be removed
in v1.4.0.
Args:
pred: estimated probabilities, with shape [N, C]
target: ground-truth labels, with shape [N,]
sample_weight: sample weights
num_classes: number of classes (default: None, computes automatically from data)
Return:
Tensor containing ROCAUC score
Example:
>>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
... [0.05, 0.85, 0.05, 0.05],
... [0.05, 0.05, 0.85, 0.05],
... [0.05, 0.05, 0.05, 0.85]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> multiclass_auroc(pred, target, num_classes=4)
tensor(0.6667)
"""
rank_zero_warn(
"This `multiclass_auroc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auroc import auroc`."
" It will be removed in v1.4.0", DeprecationWarning)
if not torch.allclose(pred.sum(dim=1), torch.tensor(1.0)):
raise ValueError(
"Multiclass AUROC metric expects the target scores to be"
" probabilities, i.e. they should sum up to 1.0 over classes")
if torch.unique(target).size(0) != pred.size(1):
raise ValueError(
f"Number of classes found in in 'target' ({torch.unique(target).size(0)})"
f" does not equal the number of columns in 'pred' ({pred.size(1)})."
" Multiclass AUROC is not defined when all of the classes do not"
" occur in the target labels.")
if num_classes is not None and num_classes != pred.size(1):
raise ValueError(
f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal"
f" the number of classes passed in 'num_classes' ({num_classes}).")
return __auroc(preds=pred,
target=target,
sample_weights=sample_weight,
num_classes=num_classes)
def multiclass_auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
num_classes: Optional[int] = None,
) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.auroc`. Will be removed in v1.4.0.
"""
rank_zero_warn(
"This `multiclass_auroc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auroc import auroc`."
" It will be removed in v1.4.0", DeprecationWarning
)
return __auroc(preds=pred, target=target, sample_weights=sample_weight, num_classes=num_classes)
def auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
max_fpr: float = None,
) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.auroc`. Will be removed in v1.4.0.
"""
rank_zero_warn(
"This `auroc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auroc import auroc`."
" It will be removed in v1.4.0", DeprecationWarning
)
return __auroc(
preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label, max_fpr=max_fpr, num_classes=1
)
def auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
max_fpr: float = None,
) -> torch.Tensor:
"""
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.auroc.auroc`. Will be removed
in v1.4.0.
Args:
pred: estimated probabilities
target: ground-truth labels
sample_weight: sample weights
pos_label: the label for the positive class
max_fpr: If not ``None``, calculates standardized partial AUC over the
range [0, max_fpr]. Should be a float between 0 and 1.
Return:
Tensor containing ROCAUC score
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 1, 0])
>>> auroc(x, y)
tensor(0.5000)
"""
rank_zero_warn(
"This `auroc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auroc import auroc`."
" It will be removed in v1.4.0", DeprecationWarning)
return __auroc(preds=pred,
target=target,
sample_weights=sample_weight,
pos_label=pos_label,
max_fpr=max_fpr,
num_classes=1)
