I am aware of  imbalance-learn lib and yes, I tried using it but unfortunately my model preprocessing is different from what they use and I have no confidence that it would dramatically improve the score because I am already using random undersampling and I also implemented ensemble learning trying 5 up to 40 models trained on different parts of the data with majority voting. It improved the f1 score only slightly. Using sklearn however does not answer the question.
So, last thing I want to try is loss function weights to be sure that feed forward network is not capable of classifying this data well enough.