AUC

Module Interface

class torchmetrics.AUC(reorder=False, compute_on_step=None, **kwargs)

Computes Area Under the Curve (AUC) using the trapezoidal rule

Forward accepts two input tensors that should be 1D and have the same number of elements

Parameters

• reorder (bool) – AUC expects its first input to be sorted. If this is not the case, setting this argument to True will use a stable sorting algorithm to sort the input in descending order

• 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.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()

Computes AUC based on inputs passed in to update previously.

Return type

Tensor

update(preds, target)

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.auc(x, y, reorder=False)

Computes Area Under the Curve (AUC) using the trapezoidal rule.

Parameters

• x (Tensor) – x-coordinates, must be either increasing or decreasing

• y (Tensor) – y-coordinates

• reorder (bool) – if True, will reorder the arrays to make it either increasing or decreasing

Return type

Tensor

Returns

Tensor containing AUC score

Raises

• ValueError – If both x and y tensors are not 1d.

• ValueError – If both x and y don’t have the same numnber of elements.

• ValueError – If x tesnsor is neither increasing nor decreasing.

Example

>>> from torchmetrics.functional import auc
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 2, 2])
>>> auc(x, y)
tensor(4.)
>>> auc(x, y, reorder=True)
tensor(4.)