Incorrect value of AUROC when plotting a `PrecisionRecallCurve` metric with `score=True` about torchmetrics HOT 2 CLOSED

Hi @tanhevg, thanks for raising this issue and sorry for the slow response from my side.
The overall problem was that when calculating the auc in the plot method of PrecisionRecallCurve there was a assumption that the x input of the curve is in ascending order but for PR-curve it is in descending order. This should be taken care of now. This should make sure that regardless of method of what way you calculate the area the values will be similar.

However, there will still be a difference in the values compared to BinaryAveragePrecision. The reason for this is that BinaryAveragePrecision uses one type of curve interpolation (as stated in the docs) whereas for PrecisionRecallCurve it uses the trapezoidal rule which will give difference in value.
For the example provided the updated code gives

I added some notes about this to the documentation in PR #2437 which also mentions the fix above.

from torchmetrics.

Thanks for the update, @SkafteNicki . Minor discrepancies between AUROC and BinaryAveragePrecision should not be a problem.

Does the PR also add labels to the axes? I had a quick look at the changes and I don't think it does. It is a bit confusing now, especially after the axes were swapped between v1.2.1 and v1.3.1

Also, there are no changes to the tests in the PR, meaning nothing prevents this or similar issues from reappearing in the future.

from torchmetrics.