ENH add weights to GroupLasso to provide support for an Adaptive GroupLasso

[sehoff]

Hi,
thanks for this great package! Does the group-lasso method allow for different penalizations for different groups, such that one could fit an adaptive group lasso, i.e., applying different weights in the second call to the method? (reference for adapative group lasso: https://www.sciencedirect.com/science/article/abs/pii/S0167947308002582)

If not, would it be possible to extend the method (easily)?

Thank you.

[mathurinm]

Hi !
Thanks for the interest. The feature is not currently supported, but it's quite easy to implement a weights attribute that can be changed after each fit ad affects the strength of each penalty (as is currently done in the Lasso class). I should be able to implement it next week

[mathurinm]

(as long as you don't need some of these weights to be 0, because celer won't support that, see #185)

[sehoff]

I really appreciate your fast answer and implementation. All my weights will be positive!

[cruyffturn]

Hi ! Thanks for the interest. The feature is not currently supported, but it's quite easy to implement a weights attribute that can be changed after each fit ad affects the strength of each penalty (as is currently done in the Lasso class). I should be able to implement it next week

I thought the celer_path allows group weights as it's indicated in here.

[mathurinm]

@cruyffturn thanks for pointing this out; it is a mistake in the docstring as weights are not passed to the solver inside the loop over alphas here: https://github.com/mathurinm/celer/blob/main/celer/homotopy.py#L296
#223 will fix this soon.