Github user sethah commented on the issue:
https://github.com/apache/spark/pull/17094
@MLnick I completely agree about the leaky regularization abstraction. In
fact, I think the function composition feature would make it easy to get rid of
that problem. Consider:
In the standardized features case we want to compute dL/dBj_std where
Bj_std = Bj / sigmaj. Define g(x) = sum(x_j^2) (l2 reg) and f(y) = y_j /
sigma_j (standardization). Then we could do `val h = g.compose(f)` to give a
loss function that provides the derivative of h as dg/df * df/dx. I hope that
made some sense. Right now, it seems an over-engineered solution to me since we
only use this function in a few places. It's definitely more elegant and more
general, but I'd prefer to do that as a follow up or if we decide to keep
building new algorithms using these abstractions.
Can you expand on your second point? Thanks!
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