{"id":13737565,"url":"https://github.com/mathurinm/celer","last_synced_at":"2025-04-04T14:03:31.375Z","repository":{"id":38868952,"uuid":"122246365","full_name":"mathurinm/celer","owner":"mathurinm","description":"Fast solver for L1-type problems: Lasso, sparse Logisitic regression, Group Lasso, weighted Lasso, Multitask Lasso, etc.","archived":false,"fork":false,"pushed_at":"2024-08-01T08:27:07.000Z","size":13029,"stargazers_count":219,"open_issues_count":21,"forks_count":34,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-03-28T13:07:17.061Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://mathurinm.github.io/celer/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mathurinm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-02-20T19:37:31.000Z","updated_at":"2025-03-27T11:23:52.000Z","dependencies_parsed_at":"2024-04-08T16:28:04.899Z","dependency_job_id":"f190f136-717f-4445-8d5c-e253d41e35df","html_url":"https://github.com/mathurinm/celer","commit_stats":{"total_commits":255,"total_committers":12,"mean_commits":21.25,"dds":"0.49411764705882355","last_synced_commit":"c9faa5089dbb63f1a0bcf091c1c58b615c0ada57"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mathurinm%2Fceler","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mathurinm%2Fceler/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mathurinm%2Fceler/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mathurinm%2Fceler/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mathurinm","download_url":"https://codeload.github.com/mathurinm/celer/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247190231,"owners_count":20898700,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T03:01:53.581Z","updated_at":"2025-04-04T14:03:31.357Z","avatar_url":"https://github.com/mathurinm.png","language":"Python","readme":"# celer\n\n![build](https://github.com/mathurinm/celer/workflows/build/badge.svg)\n![coverage](https://codecov.io/gh/mathurinm/celer/branch/main/graphs/badge.svg?branch=main)\n![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)\n[![Downloads](https://static.pepy.tech/badge/celer)](https://pepy.tech/project/celer)\n[![Downloads](https://pepy.tech/badge/celer/month)](https://pepy.tech/project/celer)\n[![PyPI version](https://badge.fury.io/py/celer.svg)](https://pypi.org/project/celer)\n\n\n``celer`` is a Python package that solves Lasso-like problems and provides estimators that follow the ``scikit-learn`` API. Thanks to a tailored implementation, ``celer`` provides a fast solver that tackles large-scale datasets with millions of features **up to 100 times faster than ``scikit-learn``**.\n\nCurrently, the package handles the following problems:\n\n\n| Problem                       | Support Weights | Native cross-validation\n| -----------                   | -----------     |----------------\n| Lasso                         | ✓               | ✓\n| ElasticNet                    | ✓               | ✓\n| Group Lasso                   | ✓               | ✓\n| Multitask Lasso               | ✕               | ✓\n| Sparse Logistic regression    | ✕               | ✕\n\nIf you are interested in other models, such as non convex penalties (SCAD, MCP), sparse group lasso, group logistic regression, Poisson regression, Tweedie regression, have a look at our companion package [``skglm``](https://github.com/scikit-learn-contrib/skglm)\n\n## Cite\n\n``celer`` is licensed under the [BSD 3-Clause](https://github.com/mathurinm/celer/blob/main/LICENSE). Hence, you are free to use it.\nIf you do so, please cite:\n\n\n```bibtex\n@InProceedings{pmlr-v80-massias18a,\n  title     = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},\n  author    = {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},\n  booktitle = {Proceedings of the 35th International Conference on Machine Learning},\n  pages     = {3321--3330},\n  year      = {2018},\n  volume    = {80},\n}\n\n@article{massias2020dual,\n  author  = {Mathurin Massias and Samuel Vaiter and Alexandre Gramfort and Joseph Salmon},\n  title   = {Dual Extrapolation for Sparse GLMs},\n  journal = {Journal of Machine Learning Research},\n  year    = {2020},\n  volume  = {21},\n  number  = {234},\n  pages   = {1-33},\n  url     = {http://jmlr.org/papers/v21/19-587.html}\n}\n```\n\n## Why ``celer``?\n\n``celer`` is specially designed to handle Lasso-like problems which makes it a fast solver of such problems.\nIn particular, it comes with tools such as:\n\n- automated parallel cross-validation\n- support of sparse and dense data\n- optional feature centering and normalization\n- unpenalized intercept fitting\n\n``celer`` also provides easy-to-use estimators as it is designed under the ``scikit-learn`` API.\n\n\n\n## Get started\n\nTo get started, install ``celer`` via pip\n\n```shell\npip install -U celer\n```\n\nOn your python console,\nrun the following commands to fit a Lasso estimator on a toy dataset.\n\n```python\n\u003e\u003e\u003e from celer import Lasso\n\u003e\u003e\u003e from celer.datasets import make_correlated_data\n\u003e\u003e\u003e X, y, _ = make_correlated_data(n_samples=100, n_features=1000)\n\u003e\u003e\u003e estimator = Lasso()\n\u003e\u003e\u003e estimator.fit(X, y)\n```\n\nThis is just a starter example.\nMake sure to browse [``celer`` documentation ](https://mathurinm.github.io/celer/) to learn more about its features.\nTo get familiar with [``celer`` API](https://mathurinm.github.io/celer/api.html), you can also explore the gallery of examples\nwhich includes examples on real-life datasets as well as timing comparisons with other solvers.\n\n\n\n## Contribute to celer\n\n``celer`` is an open-source project and hence relies on community efforts to evolve.\nYour contribution is highly valuable and can come in three forms\n\n- **bug report:** you may encounter a bug while using ``celer``. Don't hesitate to report it on the [issue section](https://github.com/mathurinm/celer/issues).\n- **feature request:** you may want to extend/add new features to ``celer``. You can use the [issue section](https://github.com/mathurinm/celer/issues) to make suggestions.\n- **pull request:** you may have fixed a bug, enhanced the documentation, ... you can submit a [pull request](https://github.com/mathurinm/celer/pulls) and we will respond asap.\n\nFor the last mean of contribution, here are the steps to help you setup ``celer`` on your local machine:\n\n1. Fork the repository and afterwards run the following command to clone it on your local machine\n\n```shell\ngit clone https://github.com/{YOUR_GITHUB_USERNAME}/celer.git\n```\n\n2. ``cd`` to ``celer`` directory and install it in edit mode by running\n\n```shell\ncd celer\npip install -e .\n```\n\n3. To run the gallery examples and build the documentation, run the following\n\n```shell\ncd doc\npip install -e .[doc]\nmake html\n```\n\n\n\n## Further links\n\n- https://mathurinm.github.io/celer/\n- https://arxiv.org/abs/1802.07481\n- https://arxiv.org/abs/1907.05830\n\n","funding_links":[],"categories":["Python","Sklearn实用程序"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmathurinm%2Fceler","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmathurinm%2Fceler","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmathurinm%2Fceler/lists"}