talksaboutthings

I'm thinking about implementing a few architectures (starting from MLP) in PyTorch in such a way that they can be used just like the equivalent sklearn model (i.e. same overall class structure and method naming convention). My rationale is that this will offer the extensibility into PyTorch-only features (GPU compute, learning rate schedulers, etc.) while allowing people to stick to the sklearn format they know and love. Has this been done already? Would this be useful to any of you guys?

Does anyone know of any precedent for selectively assigning a higher regularization penalty to some features than to others?

The context of my asking this is that I am currently thinking of adding some polynomial transformations of features to my model (the proper term for this escapes me at the moment, sorry... lifting?). The linear model already over-fits a little bit, but it also works alright, so I am thinking lasso regression might zero out some actually useful coefficients on the linear terms. Thus I wonder, what would happen if I applied an l1 or l2 penalty to just the higher order features I add, and what if I scale the penalty to be higher on the higher degree terms?

Does anyone