Metadata-Version: 2.1
Name: glmnet
Version: 2.2.0
Summary: Python wrapper for glmnet
Home-page: https://github.com/civisanalytics/python-glmnet
Author: Civis Analytics Inc
Author-email: opensource@civisanalytics.com
License: UNKNOWN
Description: Python GLMNET
        =============
        
        |CircleCI| |Conda| |PyPI| |PyVersions|
        
        .. |CircleCI| image:: https://circleci.com/gh/civisanalytics/python-glmnet.svg?style=svg
            :target: https://circleci.com/gh/civisanalytics/python-glmnet
            :alt: Build status
        
        .. |Conda| image:: https://anaconda.org/conda-forge/glmnet/badges/version.svg
           :target: https://anaconda.org/conda-forge/glmnet
           :alt: Latest version on conda forge
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/glmnet.svg
           :target: https://pypi.org/project/glmnet/
           :alt: Latest version on PyPI
        
        .. |PyVersions| image:: https://img.shields.io/pypi/pyversions/glmnet.svg
           :target: https://pypi.org/project/glmnet/
           :alt: Supported python versions for python-glmnet
        
        This is a Python wrapper for the fortran library used in the R package
        `glmnet <http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
        While the library includes linear, logistic, Cox, Poisson, and
        multiple-response Gaussian, only linear and logistic are implemented in
        this package.
        
        The API follows the conventions of
        `Scikit-Learn <http://scikit-learn.org/stable/>`__, so it is expected to
        work with tools from that ecosystem.
        
        Installation
        ------------
        
        requirements
        ~~~~~~~~~~~~
        
        ``python-glmnet`` requires Python version >= 3.6, ``scikit-learn``, ``numpy``,
        and ``scipy``. Installation from source or via ``pip`` requires a Fortran compiler.
        
        conda
        ~~~~~
        
        .. code:: bash
        
            conda install -c conda-forge glmnet
        
        
        pip
        ~~~
        
        .. code:: bash
        
            pip install glmnet
        
        
        source
        ~~~~~~
        
        ``glmnet`` depends on numpy, scikit-learn and scipy.
        A working Fortran compiler is also required to build the package.
        For Mac users, ``brew install gcc`` will take care of this requirement.
        
        .. code:: bash
        
            git clone git@github.com:civisanalytics/python-glmnet.git
            cd python-glmnet
            python setup.py install
        
        Usage
        -----
        
        General
        ~~~~~~~
        
        By default, ``LogitNet`` and ``ElasticNet`` fit a series of models using
        the lasso penalty (α = 1) and up to 100 values for λ (determined by the
        algorithm). In addition, after computing the path of λ values,
        performance metrics for each value of λ are computed using 3-fold cross
        validation. The value of λ corresponding to the best performing model is
        saved as the ``lambda_max_`` attribute and the largest value of λ such
        that the model performance is within ``cut_point * standard_error`` of
        the best scoring model is saved as the ``lambda_best_`` attribute.
        
        The ``predict`` and ``predict_proba`` methods accept an optional
        parameter ``lamb`` which is used to select which model(s) will be used
        to make predictions. If ``lamb`` is omitted, ``lambda_best_`` is used.
        
        Both models will accept dense or sparse arrays.
        
        Regularized Logistic Regression
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            from glmnet import LogitNet
        
            m = LogitNet()
            m = m.fit(x, y)
        
        Prediction is similar to Scikit-Learn:
        
        .. code:: python
        
            # predict labels
            p = m.predict(x)
            # or probability estimates
            p = m.predict_proba(x)
        
        Regularized Linear Regression
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            from glmnet import ElasticNet
        
            m = ElasticNet()
            m = m.fit(x, y)
        
        Predict:
        
        .. code:: python
        
            p = m.predict(x)
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6.*
Description-Content-Type: text/x-rst
