nn_aum_loss: AUM loss

In torch: Tensors and Neural Networks with 'GPU' Acceleration

AUM loss

Description

Creates a criterion that measures the Area under the Min(FPR, FNR) (AUM) between each element in the input pred_tensor and target label_tensor.

Usage

nn_aum_loss()

Details

This is used for measuring the error of a binary reconstruction within highly unbalanced dataset, where the goal is optimizing the ROC curve. Note that the targets label_tensor should be factor level of the binary outcome, i.e. with values 1L and 2L.

References

J. Hillman, T.D. Hocking: Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection https://jmlr.org/papers/volume24/21-0751/21-0751.pdf

Examples

if (torch_is_installed()) {

loss <- nn_aum_loss()
input <- torch_randn(4, 6, requires_grad = TRUE)
target <- input > 1.5
output <- loss(input, target)
output$backward()

}

torch documentation built on Aug. 21, 2025, 5:50 p.m.