Metadata-Version: 1.1
Name: sklearn-contrib-lightning
Version: 0.2.0
Summary: Large-scale sparse linear classification, regression and ranking in Python
Home-page: https://github.com/scikit-learn-contrib/lightning
Author: Mathieu Blondel
Author-email: mathieu@mblondel.org
License: new BSD
Download-URL: https://github.com/scikit-learn-contrib/lightning
Description: .. -*- mode: rst -*-
        
        .. image:: https://travis-ci.org/scikit-learn-contrib/lightning.svg?branch=master
            :target: https://travis-ci.org/scikit-learn-contrib/lightning
        
        .. image:: https://ci.appveyor.com/api/projects/status/mmm0llccmvn5iooq?svg=true
            :target: https://ci.appveyor.com/project/fabianp/lightning-bpc6r/branch/master
        
        lightning
        ==========
        
        lightning is a library for large-scale linear classification, regression and
        ranking in Python.
        
        Highlights:
        
        - follows the `scikit-learn <http://scikit-learn.org>`_ API conventions
        - supports natively both dense and sparse data representations
        - computationally demanding parts implemented in `Cython <http://cython.org>`_
        
        Solvers supported:
        
        - primal coordinate descent
        - dual coordinate descent (SDCA, Prox-SDCA)
        - SGD, AdaGrad, SAG, SAGA, SVRG
        - FISTA
        
        Example
        -------
        
        Example that shows how to learn a multiclass classifier with group lasso
        penalty on the News20 dataset (c.f., `Blondel et al. 2013
        <http://www.mblondel.org/publications/mblondel-mlj2013.pdf>`_):
        
        .. code-block:: python
        
            from sklearn.datasets import fetch_20newsgroups_vectorized
            from lightning.classification import CDClassifier
        
            # Load News20 dataset from scikit-learn.
            bunch = fetch_20newsgroups_vectorized(subset="all")
            X = bunch.data
            y = bunch.target
        
            # Set classifier options.
            clf = CDClassifier(penalty="l1/l2",
                               loss="squared_hinge",
                               multiclass=True,
                               max_iter=20,
                               alpha=1e-4,
                               C=1.0 / X.shape[0],
                               tol=1e-3)
        
            # Train the model.
            clf.fit(X, y)
        
            # Accuracy
            print(clf.score(X, y))
        
            # Percentage of selected features
            print(clf.n_nonzero(percentage=True))
        
        Dependencies
        ------------
        
        lightning requires Python >= 2.7, setuptools, Numpy >= 1.3, SciPy >= 0.7 and
        scikit-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.
        
        Installation
        ------------
        
        Precompiled binaries for the stable version of lightning are available for the main platforms and can be installed using pip::
        
            pip install sklearn-contrib-lightning
        
        or conda::
        
            conda install -c https://conda.anaconda.org/scikit-learn-contrib lightning
        
        
        The 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::
        
          git clone https://github.com/scikit-learn-contrib/lightning.git
          cd lightning
          python setup.py build
          sudo python setup.py install
        
        Documentation
        -------------
        
        http://contrib.scikit-learn.org/lightning/
        
        On Github
        ---------
        
        https://github.com/scikit-learn-contrib/lightning
        
        
        Authors
        -------
        
        - Mathieu Blondel, 2012-present
        - Manoj Kumar, 2015-present
        - Arnaud Rachez, 2016-present
        - Fabian Pedregosa, 2016-present
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
