Data augmentation via Nearest Neighbour algorithms

[Catarina-Alves]

It could be nice to include a class that encapsulates data augmentation via Nearest Neighbour-inspired algorithms such as SMOTE (Synthetic Minority Over-sampling Technique), ADASYN etc. @tallamjr developed some code for this, and it is saved in utils/imblearn_augment.py.

I propose to implement this data augmentation methodology in snaugment. This involves testing and developing unit tests. Note that, in previous analysis, we found that SMOTE augmentation leads to information leaks in the classification step. Thus this must be checked when implementing this augmentation.

File: snaugment.py, utils/imblearn_augment.py

[Catarina-Alves]

While we do not find this code to work for our imbalanced problem, it might be useful for someone else.