Improvements for WeightedLasso class

[mathurinm]

• [x] find new name for class as it will handle enet
• [x] add support for generic triplet penalty(w, alpha), prox(w[j], alpha, stepsize), kkt_violation(XtR, alpha) to handle any penalty (maybe we should pass a Penalty jit class instead ?)
• [ ] be aware that WeightedLasso needs a array to work correctly
• [ ] pass scikitlearn's check_estimator
• [x] in path, keep XtR (to be called gradient later on, for generic datafits) up to date (it's computed at first iteration for WS creation, and before exit to check KKT)

[mathurinm]

@QB3 I have edited the todolist for monday's sprint, feel free to add stuff. I think having a functional enet at the end of the day would be nice and useful for the community !