[D] Precision-recall to evaluate imbalanced datasets?

[Many sources](https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-imbalanced-classification/) say precision-recall is a good way to evaluate a classier for imbalanced dataset.
However, when I have much more positive samples than negatives (tp >> fp,fn) this measurement is saturated and we get a perfect AUC.

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1. Should I over-sample (or under-sample) the dataset before calculating the precision-recall curve?
2. What other methods I can use to tune a threshold for imbalance dataset?


https://preview.redd.it/rez8qncks9rc1.png?width=302&format=png&auto=webp&s=1f16cdbc3f98f40aa58ad21768686f45cfa6cf60

auc dataset however machinelearning measurement positive precision recall sample samples threshold