Fast algorithm to solve Lasso-like problems with dual extrapolation. Currently, the package handles the following problems: Lasso, Sparse Logistic regression, Group Lasso and Multitask Lasso. The estimators follow the scikit-learn API, come with automated cross-validation, and support sparse and dense data with feature centering and normalization. The solvers used allow for solving large scale problems with millions of features.
Please visit https://mathurinm.github.io/celer/ for the latest version of the documentation.
Assuming you have a working Python environment, e.g. with Anaconda you can install celer with pip.
From a console or terminal install celer with pip:
pip install -U celer
From a console or terminal clone the repository and install Celer:
git clone https://github.com/mathurinm/celer.git cd celer/ pip install -e .
To build the documentation you will need to run:
pip install -U sphinx_gallery sphinx_bootstrap_theme cd doc/ make html
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.
All dependencies are in the ./requirements.txt file.
If you use this code, please cite:
@InProceedings{pmlr-v80-massias18a,
title = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
author = {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {3321--3330},
year = {2018},
volume = {80},
}
@article{massias2019dual,
title={Dual Extrapolation for Sparse Generalized Linear Models},
author={Massias, Mathurin and Vaiter, Samuel and Gramfort, Alexandre and Salmon, Joseph},
journal={arXiv preprint arXiv:1907.05830},
year={2019}
}
ArXiv links: