{ "info": { "author": "Mathurin Massias", "author_email": "mathurin.massias@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "celer\n=====\n\n|image0| |image1|\n\nFast algorithm to solve the Lasso with dual extrapolation\n\nDocumentation\n=============\n\nPlease visit https://mathurinm.github.io/celer/ for the latest version\nof the documentation.\n\nInstall the released version\n============================\n\nAssuming you have a working Python environment, e.g. with Anaconda you\ncan `install celer with pip `__.\n\nFrom a console or terminal install celer with pip:\n\n::\n\n pip install -U celer\n\nTo setup a fully functional environment we recommend you download this\n`conda\nenvironment `__\nand install it with:\n\n::\n\n conda env create --file environment.yml\n\nInstall the development version\n===============================\n\nFrom a console or terminal clone the repository and install CELER:\n\n::\n\n git clone https://github.com/mathurinm/celer.git\n cd celer/\n conda env create --file environment.yml\n source activate celer-env\n pip install --no-deps -e .\n\nDemos & Examples\n================\n\nYou find on the documentation examples on the `Leukemia\ndataset `__\n(comparison with scikit-learn) and on the `Finance/log1p\ndataset `__\n(more significant, but it takes times to download the data, preprocess\nit, and compute the path).\n\nDependencies\n============\n\nAll dependencies are in ``./environment.yml``\n\nCite\n====\n\nIf you use this code, please cite:\n\n::\n\n @InProceedings{pmlr-v80-massias18a,\n title = \t {Celer: a Fast Solver for the Lasso with Dual Extrapolation},\n author = \t {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},\n booktitle = \t {Proceedings of the 35th International Conference on Machine Learning},\n pages = \t {3321--3330},\n year = \t {2018},\n volume = \t {80},\n }\n\nArXiv link: https://arxiv.org/abs/1802.07481\n\n.. |image0| image:: 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