{ "info": { "author": "Civis Analytics Inc", "author_email": "opensource@civisanalytics.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v2 (GPLv2)", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering" ], "description": "Python GLMNET\n=============\n\nThis is a Python wrapper for the fortran library used in the R package\n`glmnet `__.\nWhile the library includes linear, logistic, Cox, Poisson, and\nmultiple-response Gaussian, only linear and logistic are implemented in\nthis package.\n\nThe API follows the conventions of\n`Scikit-Learn `__, so it is expected to\nwork with tools from that ecosystem.\n\nInstallation\n------------\n\nrequirements\n~~~~~~~~~~~~\n\n``python-glmnet`` requires Python version >= 3.4, ``scikit-learn``, ``numpy``,\nand ``scipy``. Installation from source or via ``pip`` requires a Fortran compiler.\n\nconda\n~~~~~\n\n.. code:: bash\n\n conda install -c conda-forge glmnet\n\n\npip\n~~~\n\n.. code:: bash\n\n pip install glmnet\n\n\nsource\n~~~~~~\n\n``glmnet`` depends on numpy, scikit-learn and scipy. A working Fortran\ncompiler is also required to build the package, for Mac users,\n``brew install gcc`` will take care of this requirement.\n\n.. code:: bash\n\n git clone git@github.com:civisanalytics/python-glmnet.git\n cd python-glmnet\n python setup.py install\n\nUsage\n-----\n\nGeneral\n~~~~~~~\n\nBy default, ``LogitNet`` and ``ElasticNet`` fit a series of models using\nthe lasso penalty (\u03b1 = 1) and up to 100 values for \u03bb (determined by the\nalgorithm). In addition, after computing the path of \u03bb values,\nperformance metrics for each value of \u03bb are computed using 3-fold cross\nvalidation. The value of \u03bb corresponding to the best performing model is\nsaved as the ``lambda_max_`` attribute and the largest value of \u03bb such\nthat the model performance is within ``cut_point * standard_error`` of\nthe best scoring model is saved as the ``lambda_best_`` attribute.\n\nThe ``predict`` and ``predict_proba`` methods accept an optional\nparameter ``lamb`` which is used to select which model(s) will be used\nto make predictions. If ``lamb`` is omitted, ``lambda_best_`` is used.\n\nBoth models will accept dense or sparse arrays.\n\nRegularized Logistic Regression\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n from glmnet import LogitNet\n\n m = LogitNet()\n m = m.fit(x, y)\n\nPrediction is similar to Scikit-Learn:\n\n.. code:: python\n\n # predict labels\n p = m.predict(x)\n # or probability estimates\n p = m.predict_proba(x)\n\nRegularized Linear Regression\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n from glmnet import ElasticNet\n\n m = ElasticNet()\n m = m.fit(x, y)\n\nPredict:\n\n.. code:: python\n\n p = m.predict(x)\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/civisanalytics/python-glmnet", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "glmnet", "package_url": "https://pypi.org/project/glmnet/", "platform": "", "project_url": "https://pypi.org/project/glmnet/", "project_urls": { "Homepage": "https://github.com/civisanalytics/python-glmnet" }, "release_url": "https://pypi.org/project/glmnet/2.1.1/", "requires_dist": [ "numpy (>=1.9.2)", "scikit-learn (>=0.18.0)", "scipy (>=0.14.1)" ], "requires_python": "", "summary": "Python wrapper for glmnet", "version": "2.1.1" }, "last_serial": 4930637, "releases": { "1.0.0": [ { "comment_text": "", "digests": { "md5": "4348ad6532b83c6e0477e96c7fbe87bb", "sha256": "ee5c53fa2887e47f448f7efa5a7c8d7fd6b5b0277bbe73ea971a27d6cd1dd5e7" }, "downloads": -1, "filename": "glmnet-1.0.0.tar.gz", "has_sig": false, "md5_digest": "4348ad6532b83c6e0477e96c7fbe87bb", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 102048, "upload_time": "2016-07-19T16:15:35", "url": "https://files.pythonhosted.org/packages/38/53/89c9eb398f4686efef8be30251018f183715193e7635d859130fd39f0891/glmnet-1.0.0.tar.gz" } ], "2.0.0": [ { "comment_text": "", "digests": { "md5": "e0465e76af8c22440d086f34d9ad8d5c", "sha256": "49974e6fe7d15e260e674c90de6cea8b407b7bb280e579099ca4cb5bb383dcc8" }, "downloads": -1, "filename": "glmnet-2.0.0.tar.gz", "has_sig": false, "md5_digest": "e0465e76af8c22440d086f34d9ad8d5c", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 102642, "upload_time": "2017-03-03T14:49:32", "url": "https://files.pythonhosted.org/packages/c7/97/6f92f20fc193478c5d5927396c8d691abbdaa7774fd67e8a08fdeb1a2470/glmnet-2.0.0.tar.gz" } ], "2.1.1": [ { "comment_text": "", "digests": { "md5": "1a8860a659cba3aea02d7cb899a669fa", "sha256": "903bf0d817a6f54deebc0d8dabc83e96b869bc819459de81bfddec8d720c5fd0" }, "downloads": -1, "filename": "glmnet-2.1.1-cp36-cp36m-macosx_10_7_x86_64.whl", "has_sig": false, "md5_digest": "1a8860a659cba3aea02d7cb899a669fa", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 681353, "upload_time": "2019-03-11T21:59:02", "url": "https://files.pythonhosted.org/packages/96/75/a35a1638d39799dd6af1780c23044a2ee80ef4afeb6c85cb4c4261863692/glmnet-2.1.1-cp36-cp36m-macosx_10_7_x86_64.whl" }, { "comment_text": "", "digests": { "md5": "6d44a281acafdd37330df9debb22ddb7", "sha256": "01244cf826995af0cf7cc8652947f12db36657725473c716db3c13580650c8cd" }, "downloads": -1, "filename": "glmnet-2.1.1.tar.gz", "has_sig": false, "md5_digest": "6d44a281acafdd37330df9debb22ddb7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 104723, "upload_time": "2019-03-11T21:59:46", "url": "https://files.pythonhosted.org/packages/b7/31/6e8ac40b76fb8805cbcda406b90364e2097abadcd1f91fd1066a67a66b73/glmnet-2.1.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "1a8860a659cba3aea02d7cb899a669fa", "sha256": "903bf0d817a6f54deebc0d8dabc83e96b869bc819459de81bfddec8d720c5fd0" }, "downloads": -1, "filename": "glmnet-2.1.1-cp36-cp36m-macosx_10_7_x86_64.whl", "has_sig": false, "md5_digest": "1a8860a659cba3aea02d7cb899a669fa", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 681353, "upload_time": "2019-03-11T21:59:02", "url": "https://files.pythonhosted.org/packages/96/75/a35a1638d39799dd6af1780c23044a2ee80ef4afeb6c85cb4c4261863692/glmnet-2.1.1-cp36-cp36m-macosx_10_7_x86_64.whl" }, { "comment_text": "", "digests": { "md5": "6d44a281acafdd37330df9debb22ddb7", "sha256": "01244cf826995af0cf7cc8652947f12db36657725473c716db3c13580650c8cd" }, "downloads": -1, "filename": "glmnet-2.1.1.tar.gz", "has_sig": false, "md5_digest": "6d44a281acafdd37330df9debb22ddb7", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 104723, "upload_time": "2019-03-11T21:59:46", "url": "https://files.pythonhosted.org/packages/b7/31/6e8ac40b76fb8805cbcda406b90364e2097abadcd1f91fd1066a67a66b73/glmnet-2.1.1.tar.gz" } ] }