Simple precision/recall/F-score is perfectly suitable for imbalanced data. It should be computed on the minority class of course.
- precision says how often the system is correct when predicting an instance in the minority class.
- recall says how often the system detects an instance which belongs to the minority class (this is usually the hardest part with imbalanced data)
- As usual F-score can be used as a "summary metric" of both precision and recall.
ROC and AUC (which are also based on precision/recall) can be used only with a soft classifier, i.e. when the system predicts a numerical value instead of a binary label.
Macro/micro/weighted average (usually over F-score) are irrelevant: averaging over the two classes would take into account the majority class which is very easy to predict, so it would mask the useful information about the minority class.