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\n\n\n![](\"https://travis-ci.org/BouchardLab/PyUoI.svg?branch=master\")
[](https://codecov.io/gh/BouchardLab/PyUoI)\n[](https://pyuoi.readthedocs.io/en/latest/?badge=latest)\n\n\nPyUoI contains implementations of Union of Intersections framework for a variety\nof penalized generalized linear models as well as dimensionality reductions\ntechniques such as column subset selection and non-negative matrix\nfactorization. In general, UoI is a statistical machine learning framework that\nleverages two concepts in model inference:\n\n1. Separating the selection and estimation problems to simultaneously achieve\n sparse models with low-bias and low-variance parameter estimates.\n2. Stability to perturbations in both selection and estimation.\n\n\nPyUoI is designed to function similarly to ``scikit-learn``, as it often builds\nupon ``scikit-learn``'s implementations of the aforementioned algorithms.\n\nFurther details on the UoI framework can be found in the NeurIPS paper (Bouchard et al., 2017).\n\n# Installation\n\nPyUoI will be available soon on PyPI (installable with ``pip``):\n\n```\npip install pyuoi\n```\n\nand through conda-forge (installable with ``conda``):\n\n```\nconda install -c conda-forge pyuoi\n```\n\n# Features\n\nPyUoI is split up into two modules, with the following UoI algorithms:\n\n* `linear_model` (generalized linear models)\n * Lasso penalized linear regression UoILasso.\n * Elastic-net penalized linear regression (UoIElasticNet).\n * Logistic regression (Bernoulli and multinomial) (UoILogistic).\n * Poisson regression (UoIPoisson).\n* `decomposition` (dimensionality reduction)\n * Column subset selection (UoICSS).\n * Non-negative matrix factorization (UoINMF).\n\nSimilar to `scikit-learn`, each UoI algorithm has its own Python class.\n\n# Documentation\n\nPlease see our ReadTheDocs page for an introduction to Union of Intersections, usage of PyUoI, and the API.\n\n# Copyright\n\nPyUol Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.\n\nIf you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation & Partnerships Office at IPO@lbl.gov referring to \" PyUol\" (LBNL Ref 2019-157).\"\n\nNOTICE. This software was developed under funding from the U.S. Department of Energy. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, and perform publicly and display publicly. The U.S. Government is granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.\n\n\n",
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