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Hi @zhiqiangdon, thanks for reporting this issue.
In this case it is not an error but instead an explicit design decision. We standardized all binary classification metrics to always require the target to be encoded as [0,1] where the positive class will then be the 1 class.
If you want the 0 class to be the positive class then feed targets as
BinaryAUROC()(preds, 1-target)
Sorry for the confusion. I created PR #1383 that will explicit mention that 1 is always considered the positive class.
🐛 Bug
The
pos_label
argument is missing from AUROC/BinaryAUROC after the recent cleanup: #1252I guess this is a bug, but would like confirm with you.
To Reproduce
Steps to reproduce the behavior...
Code sample
Expected behavior
Environment
conda
,pip
, build from source):Additional context
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