ENH: add enet support (l1 + L2 reg for least squares)

mathurinm / celer

Fast solver for L1-type problems: Lasso, sparse Logisitic regression, Group Lasso, weighted Lasso, Multitask Lasso, etc.

https://mathurinm.github.io/celer/

BSD 3-Clause "New" or "Revised" License

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Closed mathurinm closed 2 years ago

mathurinm commented 4 years ago

expose same parametrization as sklearn with alpha and l1_ratio
add new dual obj for enet, with soft thresholding
do not rescale theta if enet / or more simply use the "enet as rescaled Lasso" equivalence
coefficient updates : if enet, mutiply output of ST by 1 / (1 + alpha * (1 - l1_ratio) / lc[j])
[ ] add support for l1_ratio in solver
[ ] handle screening an priorization correctly
[ ] add ElasticNet and ElasticNetCV classes
[ ] add example in the doc
[ ] add it to benchmopt/benchmark_elasticnet and compare to sklearn and skglm

Badr-MOUFAD commented 2 years ago

[x] add support for l1_ratio in solver
[x] handle screening an priorization correctly
[ ] add ElasticNet and ElasticNetCV classes (partially, need to add ElasticNetCV)
[ ] add example in the doc
[ ] add it to benchmopt/benchmark_elasticnet and compare to sklearn and skglm

Badr-MOUFAD commented 2 years ago

[x] add support for l1_ratio in solver
[x] handle screening an priorization correctly
[x] add ElasticNet and ElasticNetCV classes
[ ] add example in the doc
[ ] add it to benchmopt/benchmarkelasticnet and compare to sklearn and skglm (benchoptnot working)_

Badr-MOUFAD commented 2 years ago

@mathurinm, regarding the

[ ] add example in the doc

I should get inspired from the celer/examples?

Badr-MOUFAD commented 2 years ago

[x] add support for l1_ratio in solver
[x] handle screening an priorization correctly
[x] add ElasticNet and ElasticNetCV classes
[x] add example in the doc
[ ] ~~add it to benchmopt/benchmark_elasticnet and compare to sklearn and skglm~~

With this being achieved, I think we are done with #230