Shortcuts

AUROC

Module Interface

class torchmetrics.AUROC(num_classes=None, pos_label=None, average='macro', max_fpr=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 and target tensor have the same size the input will be interpretated as multilabel and if preds have one dimension more than the target 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 problems

  • pos_label (Optional[int]) – integer determining the positive class. Default is None 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]

  • average (Optional[str]) –

    • '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 not None, calculates standardized partial AUC over the range [0, max_fpr]. Should be a float between 0 and 1.

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises
  • ValueError – If average is none of None, "macro" or "weighted".

  • ValueError – If max_fpr is not a float in the range (0, 1].

  • RuntimeError – If PyTorch version is below 1.6 since max_fpr requires torch.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.

compute()[source]

Computes AUROC based on inputs passed in to update previously.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model (probabilities, or labels)

  • target (Tensor) – Ground truth labels

Return type

None

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 and target tensor have the same size the input will be interpretated as multilabel and if preds have one dimension more than the target 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)

  • target (Tensor) – Ground truth labels

  • num_classes (Optional[int]) – integer with number of classes for multi-label and multiclass problems. Should be set to None for binary problems

  • pos_label (Optional[int]) – integer determining the positive class. Default is None 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]

  • average (Optional[str]) –

    • '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 not None, 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 a float in the range (0, 1].

  • RuntimeError – If PyTorch version is below 1.6 since max_fpr requires torch.bucketize which is not available below 1.6.

  • ValueError – If max_fpr is not set to None and the mode is not binary since partial AUC computation is not available in multilabel/multiclass.

  • ValueError – If average is none of None, "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

Tensor

Read the Docs v: stable
Versions
latest
stable
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
refactor-structure
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.