AUROC¶
Module Interface¶
- class torchmetrics.AUROC(num_classes=None, pos_label=None, average='macro', max_fpr=None, compute_on_step=None, **kwargs)[source]
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC). Works for both binary, multilabel and multiclass problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.
Forward accepts
preds
(float tensor):(N, ...)
(binary) or(N, C, ...)
(multiclass) tensor with probabilities, where C is the number of classes.target
(long tensor):(N, ...)
or(N, C, ...)
with integer labels
For non-binary input, if the
preds
andtarget
tensor have the same size the input will be interpretated as multilabel and ifpreds
have one dimension more than thetarget
tensor the input will be interpretated as multiclass.Note
If either the positive class or negative class is completly missing in the target tensor, the auroc score is meaningless in this case and a score of 0 will be returned together with an warning.
- Parameters
num_classes¶ (
Optional
[int
]) –integer with number of classes for multi-label and multiclass problems.
Should be set to
None
for binary problemspos_label¶ (
Optional
[int
]) – integer determining the positive class. Default isNone
which for binary problem is translated to 1. For multiclass problems this argument should not be set as we iteratively change it in the range[0, num_classes-1]
'micro'
computes metric globally. Only works for multilabel problems'macro'
computes metric for each class and uniformly averages them'weighted'
computes metric for each class and does a weighted-average, where each class is weighted by their support (accounts for class imbalance)None
computes and returns the metric per class
max_fpr¶ (
Optional
[float
]) – If notNone
, calculates standardized partial AUC over the range[0, max_fpr]
. Should be a float between 0 and 1.compute_on_step¶ (
Optional
[bool
]) –Forward only calls
update()
and returns None if this is set to False.Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (
Dict
[str
,Any
]) – Additional keyword arguments, see Advanced metric settings for more info.
- Raises
ValueError – If
average
is none ofNone
,"macro"
or"weighted"
.ValueError – If
max_fpr
is not afloat
in the range(0, 1]
.RuntimeError – If
PyTorch version
isbelow 1.6
sincemax_fpr
requirestorch.bucketize
which is not available below 1.6.ValueError – If the mode of data (binary, multi-label, multi-class) changes between batches.
- Example (binary case):
>>> from torchmetrics import AUROC >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34]) >>> target = torch.tensor([0, 0, 1, 1, 1]) >>> auroc = AUROC(pos_label=1) >>> auroc(preds, target) tensor(0.5000)
- Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05], ... [0.05, 0.90, 0.05], ... [0.05, 0.05, 0.90], ... [0.85, 0.05, 0.10], ... [0.10, 0.10, 0.80]]) >>> target = torch.tensor([0, 1, 1, 2, 2]) >>> auroc = AUROC(num_classes=3) >>> auroc(preds, target) tensor(0.7778)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.auroc(preds, target, num_classes=None, pos_label=None, average='macro', max_fpr=None, sample_weights=None)[source]
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
For non-binary input, if the
preds
andtarget
tensor have the same size the input will be interpretated as multilabel and ifpreds
have one dimension more than thetarget
tensor the input will be interpretated as multiclass.Note
If either the positive class or negative class is completly missing in the target tensor, the auroc score is meaningless in this case and a score of 0 will be returned together with a warning.
- Parameters
preds¶ (
Tensor
) – predictions from model (logits or probabilities)num_classes¶ (
Optional
[int
]) – integer with number of classes for multi-label and multiclass problems. Should be set toNone
for binary problemspos_label¶ (
Optional
[int
]) – integer determining the positive class. Default isNone
which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1]'micro'
computes metric globally. Only works for multilabel problems'macro'
computes metric for each class and uniformly averages them'weighted'
computes metric for each class and does a weighted-average, where each class is weighted by their support (accounts for class imbalance)None
computes and returns the metric per class
max_fpr¶ (
Optional
[float
]) – If notNone
, calculates standardized partial AUC over the range[0, max_fpr]
. Should be a float between 0 and 1.sample_weights¶ (
Optional
[Sequence
]) – sample weights for each data point
- Raises
ValueError – If
max_fpr
is not afloat
in the range(0, 1]
.RuntimeError – If
PyTorch version
is below 1.6 since max_fpr requirestorch.bucketize
which is not available below 1.6.ValueError – If
max_fpr
is not set toNone
and the mode isnot binary
since partial AUC computation is not available in multilabel/multiclass.ValueError – If
average
is none ofNone
,"macro"
or"weighted"
.
- Example (binary case):
>>> from torchmetrics.functional import auroc >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34]) >>> target = torch.tensor([0, 0, 1, 1, 1]) >>> auroc(preds, target, pos_label=1) tensor(0.5000)
- Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05], ... [0.05, 0.90, 0.05], ... [0.05, 0.05, 0.90], ... [0.85, 0.05, 0.10], ... [0.10, 0.10, 0.80]]) >>> target = torch.tensor([0, 1, 1, 2, 2]) >>> auroc(preds, target, num_classes=3) tensor(0.7778)
- Return type