skglm

skglm is a library that provide better sparse generalized linear model for scikit-learn. Its main features are:

• speed: problems with millions of features can be solved in seconds. Default solvers rely on efficient coordinate descent with numba just in time compilation.
• flexibility: virtually any combination of datafit and penalty can be implemented in a few lines of code.
• sklearn API: all estimators are drop-in replacements for scikit-learn.
• scope: support for many missing models in scikit-learn - weighted Lasso, arbitrary group penalties, non convex sparse penalties, etc.

Cite

If you use this code, please cite

@online{skglm,
    title={Beyond L1: Faster and Better Sparse Models with skglm},
    author={Q. Bertrand and Q. Klopfenstein and P.-A. Bannier and G. Gidel and M. Massias},
    year={2022},
    url={https://arxiv.org/abs/2204.07826}
}

Installing the development version

First clone the repository available at https://github.com/mathurinm/skglm:

$ git clone https://github.com/mathurinm/skglm.git
$ cd skglm/

Then, install the package with:

$ pip install -e .

To check if everything worked fine, you can do:

$ python -c 'import skglm'

and it should not give any error message.