Why would be too optimistic to compute presicion, recall and f1-score to evaluate a model trained for imbalanced classification on a balanced testing sample ?
I do not quite understand what you mean by “optimistic”. Training on a balanced set and testing on an imbalanced set is fine to me, as long as the test set model the real distribution of the data well and the classifier performs well.
However, if you want to estimate the precision on the imbalanced set based on the performance on the training set, that will not work. Precision and recall will look very different between balanced and imbalanced set.