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        "author": "Mathieu Blondel",
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            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
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            "Programming Language :: C",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": ".. -*- mode: rst -*-\n\n.. image:: https://travis-ci.org/scikit-learn-contrib/lightning.svg?branch=master\n    :target: https://travis-ci.org/scikit-learn-contrib/lightning\n\n.. image:: https://ci.appveyor.com/api/projects/status/mmm0llccmvn5iooq?svg=true\n    :target: https://ci.appveyor.com/project/fabianp/lightning-bpc6r/branch/master\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.200504.svg\n   :target: https://doi.org/10.5281/zenodo.200504\n\nlightning\n==========\n\nlightning is a library for large-scale linear classification, regression and\nranking in Python.\n\nHighlights:\n\n- follows the `scikit-learn <http://scikit-learn.org>`_ API conventions\n- supports natively both dense and sparse data representations\n- computationally demanding parts implemented in `Cython <http://cython.org>`_\n\nSolvers supported:\n\n- primal coordinate descent\n- dual coordinate descent (SDCA, Prox-SDCA)\n- SGD, AdaGrad, SAG, SAGA, SVRG\n- FISTA\n\nExample\n-------\n\nExample that shows how to learn a multiclass classifier with group lasso\npenalty on the News20 dataset (c.f., `Blondel et al. 2013\n<http://www.mblondel.org/publications/mblondel-mlj2013.pdf>`_):\n\n.. code-block:: python\n\n    from sklearn.datasets import fetch_20newsgroups_vectorized\n    from lightning.classification import CDClassifier\n\n    # Load News20 dataset from scikit-learn.\n    bunch = fetch_20newsgroups_vectorized(subset=\"all\")\n    X = bunch.data\n    y = bunch.target\n\n    # Set classifier options.\n    clf = CDClassifier(penalty=\"l1/l2\",\n                       loss=\"squared_hinge\",\n                       multiclass=True,\n                       max_iter=20,\n                       alpha=1e-4,\n                       C=1.0 / X.shape[0],\n                       tol=1e-3)\n\n    # Train the model.\n    clf.fit(X, y)\n\n    # Accuracy\n    print(clf.score(X, y))\n\n    # Percentage of selected features\n    print(clf.n_nonzero(percentage=True))\n\nDependencies\n------------\n\nlightning requires Python >= 2.7, setuptools, Numpy >= 1.3, SciPy >= 0.7 and\nscikit-learn >= 0.15. Building from source also requires Cython and a working C/C++ compiler. To run the tests you will also need nose >= 0.10.\n\nInstallation\n------------\n\nPrecompiled binaries for the stable version of lightning are available for the main platforms and can be installed using pip::\n\n    pip install sklearn-contrib-lightning\n\nor conda::\n\n    conda install -c conda-forge sklearn-contrib-lightning\n\n\nThe development version of lightning can be installed from its git repository. In this case it is assumed that you have the git version control system, a working C++ compiler, Cython and the numpy development libraries. In order to install the development version, type::\n\n  git clone https://github.com/scikit-learn-contrib/lightning.git\n  cd lightning\n  python setup.py build\n  sudo python setup.py install\n\nDocumentation\n-------------\n\nhttp://contrib.scikit-learn.org/lightning/\n\nOn Github\n---------\n\nhttps://github.com/scikit-learn-contrib/lightning\n\n\nCiting\n------\n\nIf you use this software, please cite it. Here is a BibTex snippet that you can use:\n\n\n.. code-block::\n\n  @misc{lightning_2016,\n    author       = {Blondel, Mathieu and\n                    Pedregosa, Fabian},\n    title        = {{Lightning: large-scale linear classification,\n                   regression and ranking in Python}},\n    year         = 2016,\n    doi          = {10.5281/zenodo.200504},\n    url          = {https://doi.org/10.5281/zenodo.200504}\n  }\n\n\nOther citing formats are available in `its Zenodo entry <https://doi.org/10.5281/zenodo.200504>`_ .\n\nAuthors\n-------\n\n- Mathieu Blondel, 2012-present\n- Manoj Kumar, 2015-present\n- Arnaud Rachez, 2016-present\n- Fabian Pedregosa, 2016-present\n",
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