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