Polyak averaging for test-time data-augmetation

[GilesStrong]

Current status

When data-augmentation is applied at test-time, the final prediction is based on the original data and the augmented data. This if okay, with the current data-augmentation in LUMIN, since it (should) result in physically valid events which are as likely as the original event.

Potential problem

The user, or future updates of LUMIN, may add data-augmentation which only produces data which is similar to the actual data, but is either not strictly physical, or as a differing probability of being.

Possible solution

In these cases is might be advantages to for the final prediction via Polyak averaging of the score on the original data, and on the augmented data, e.g.:

score = (beta*score on original data) + ((1-beta)*mean score on augmented data)

Beta would need to be an optional argument when calling .predict* methods of Ensemble and Model, and also Model.evaluate*. Beta could also be set as an property of e.g. HEPAugFoldYielder, and the the relevant methods could then see whether a beta had been set for the data, to avoid having to explicitly pass it every time.