{"id":19820261,"url":"https://github.com/slipguru/l1l2py","last_synced_at":"2025-05-01T11:33:37.800Z","repository":{"id":84004489,"uuid":"63789212","full_name":"slipguru/l1l2py","owner":"slipguru","description":"l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.","archived":false,"fork":false,"pushed_at":"2017-12-05T16:34:19.000Z","size":3755,"stargazers_count":9,"open_issues_count":2,"forks_count":3,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-03-28T09:13:42.441Z","etag":null,"topics":["feature-selection","fista","l1l2-regularization","machinelearning","python","variable-selection"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/slipguru.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}},"created_at":"2016-07-20T14:42:14.000Z","updated_at":"2023-06-07T07:26:03.000Z","dependencies_parsed_at":"2023-10-20T23:09:46.960Z","dependency_job_id":null,"html_url":"https://github.com/slipguru/l1l2py","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slipguru%2Fl1l2py","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slipguru%2Fl1l2py/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slipguru%2Fl1l2py/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slipguru%2Fl1l2py/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/slipguru","download_url":"https://codeload.github.com/slipguru/l1l2py/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224254579,"owners_count":17281176,"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":["feature-selection","fista","l1l2-regularization","machinelearning","python","variable-selection"],"created_at":"2024-11-12T10:22:27.999Z","updated_at":"2024-11-12T10:22:28.533Z","avatar_url":"https://github.com/slipguru.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003c!-- \u003cimg src=\"http://www.slipguru.unige.it/Software/L1L2Py/_static/l1l2py_logo.png\"\u003e\u003cbr\u003e\u003cbr\u003e BROKEN LINK--\u003e\n\u003c/p\u003e\n\n\u003cimg src=\"https://travis-ci.org/slipguru/l1l2py.svg?branch=master\" alt=\"Build Status\" /\u003e\n\n**l1l2py** is a Python package to perform variable selection by means\nof l1l2 regularization with double optimization.\n\n**l1l2py** makes use of `numpy` \u003chttp://numpy.scipy.org\u003e to provide fast\nN-dimensional array manipulation and it is the Python implementation of the\nmethod proposed and applied in [DeMol09].\n\n**l1l2py** is fully compatible with `scikit-learn` Python toolbox for machine learning \u003chttp://scikit-learn.org/stable/\u003e.\n\n**l1l2py** is a project of SLIPGURU - Statistical Learning and Image Processing\nGenoa University Research Group - Via Dodecaneso, 35 - 16146 Genova, ITALY\n\u003chttp://slipguru.unige.it/\u003e.\n\n**l1l2py** is free software. It is licensed under the GNU General Public\nLicense (GPL) version 3 \u003chttp://www.gnu.org/licenses/gpl.html\u003e.\n\n## Installation\n\n**l1l2py** supports Python 2.7\n\n### Pip installation\n`$ pip install L1L2py`\n\n### Installing from sources\n```bash\n$ git clone https://github.com/slipguru/l1l2py\n$ cd l1l2py\n$ python setup.py install\n```\n\n## Try L1L2py\n**l1l2py** can be used to solve both regression and classification problems.\n\n### Regression\n```python\n\u003e\u003e\u003e from l1l2py.linear_model import L1L2\n\u003e\u003e\u003e from sklearn.datasets import load_boston\n\u003e\u003e\u003e X, y = load_boston(return_X_y=True)\n\u003e\u003e\u003e l1l2 = L1L2(tau=1, mu=0.5).fit(X, y)\n\u003e\u003e\u003e l1l2\nL1L2(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5, max_iter=10000,\n   mu=0.5, normalize=False, positive=False, precompute=False,\n   random_state=None, selection='cyclic', tau=1, tol=0.0001, use_gpu=False,\n   warm_start=False)\n\u003e\u003e\u003e l1l2.coef_\narray([-0.07871197,  0.05147779, -0.00579328,  0.        , -0.        ,\n        1.03468713,  0.01992332, -0.74193449,  0.30231875, -0.01640304,\n       -0.77808095,  0.00851397, -0.75325201])\n```\n\n### Classification\n```python\n\u003e\u003e\u003e from l1l2py.classification import L1L2Classifier\n\u003e\u003e\u003e from sklearn.datasets import load_breast_cancer\n\u003e\u003e\u003e X, y = load_breast_cancer(return_X_y=True)\n\u003e\u003e\u003e l1l2 = L1L2Classifier(tau=1, mu=0.5).fit(X, y)\n\u003e\u003e\u003e l1l2\nL1L2Classifier(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n   max_iter=10000, mu=0.5, normalize=False, positive=False,\n   precompute=False, random_state=None, selection='cyclic', tau=1,\n   threshold=1e-16, tol=0.0001, use_gpu=False, warm_start=False)\n\u003e\u003e\u003e l1l2.coef_\narray([[-0.        , -0.        ,  0.00013437,  0.00046001, -0.        ,\n        -0.        , -0.        , -0.        , -0.        , -0.        ,\n        -0.        , -0.        , -0.        ,  0.        , -0.        ,\n        -0.        , -0.        , -0.        , -0.        , -0.        ,\n        -0.        , -0.01317881, -0.03120279,  0.00032235, -0.        ,\n        -0.        , -0.        , -0.        , -0.        , -0.        ]])\n```\n\n## Help and support\nHTML documentation [L1L2py documentation](http://www.slipguru.unige.it/Software/L1L2Py/).\n\n## Reference\n[DeMol09] C. De Mol, S. Mosci, M. Traskine, A. Verri,\n         \"A Regularized Method for Selecting Nested Group of Genes from\n         Microarray Data\"\n        Journal of Computational Biology, vol. 16, pp. 677-690, 2009.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslipguru%2Fl1l2py","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fslipguru%2Fl1l2py","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslipguru%2Fl1l2py/lists"}