skglm

skglm is a library that provides better sparse generalized linear models 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.
• scikit-learn 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.

Currently, the package handles any combination of the following datafits:

• quadratic
• logistic loss
• multitask quadratic

and the following penalties:

• L1 norm
• weighted L1 norm
• L1 + L2 squared norm (elastic net)
• MCP
• L05 and L2/3 penalties

The estimators follow the scikit-learn API, come with automated parallel cross-validation, and support both sparse and dense data.

Documentation

Please visit https://contrib.scikit-learn.org/skglm/ for the latest version of the documentation.

Install and work with the development version

First clone the repository available at https://github.com/scikit-learn-contrib/skglm:

$ git clone https://github.com/scikit-learn-contrib/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.

Demos & Examples

In the example section of the documentation, you will find numerous examples on real-life datasets, timing comparison with other estimators, easy and fast ways to perform cross-validation, etc.

Dependencies

All dependencies are specified in the setup.py file. They are installed automatically when pip install -e . is run.

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}
}

ArXiv links:

• https://arxiv.org/pdf/2204.07826.pdf