Add max false positive rate option to tf.keras.metrics.AUC

[9las]

System information.

TensorFlow version (you are using): 2.12.0 Are you willing to contribute it (Yes/No) : No

Describe the feature and the current behavior/state.

I would like an option for tf.keras.metrics.AUC to control the upper bound when integrating the ROC curve. The standardized partial AUC over the false positive rate range [0, upper bound] should be returned. The option should work like the max_fpr option for sklearn.metrics.roc_auc_score.

The option would be useful if you e.g. are only interested in high model performance for low false positive rates. Then you could calculate AUC over e.g. the range [0, 0.1].

Will this change the current api? How? I do not know

Who will benefit from this feature? Keras users

Contributing

• Do you want to contribute a PR? (yes/no): no
• If yes, please read this page for instructions
• Briefly describe your candidate solution(if contributing):

Edit I originally wrote that the option should control the upper bound of the false positive rate. But I think it is better if the option control both the upper and lower bound so that the option is more general.