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https://github.com/glm-tools/pyglmnet

Python implementation of elastic-net regularized generalized linear models
https://github.com/glm-tools/pyglmnet

data-science elastic-net glm lasso machine-learning python

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Python implementation of elastic-net regularized generalized linear models

Host: GitHub
URL: https://github.com/glm-tools/pyglmnet
Owner: glm-tools
License: mit
Created: 2016-04-02T15:46:24.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2023-08-17T17:49:07.000Z (over 1 year ago)
Last Synced: 2024-10-31T07:04:46.594Z (about 2 months ago)
Topics: data-science, elastic-net, glm, lasso, machine-learning, python
Language: Python
Homepage: http://glm-tools.github.io/pyglmnet/
Size: 7.31 MB
Stars: 280
Watchers: 13
Forks: 84
Open Issues: 39
Metadata Files:
- Readme: README.rst
- License: LICENSE

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README

        pyglmnet

========

A python implementation of elastic-net regularized generalized linear models

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

|License| |Travis| |Codecov| |Circle| |Gitter| |DOI| |JOSS|

`[Documentation (stable version)]`_ `[Documentation (development version)]`_

.. image:: https://user-images.githubusercontent.com/15852194/67919367-70482600-fb76-11e9-9b86-891969bd2bee.jpg

-  Pyglmnet provides a wide range of noise models (and paired canonical

   link functions): ``'gaussian'``, ``'binomial'``, ``'probit'``,

   ``'gamma'``, '``poisson``', and ``'softplus'``.

-  It supports a wide range of regularizers: ridge, lasso, elastic net,

   `group

   lasso `__,

   and `Tikhonov

   regularization `__.

-  We have implemented a cyclical coordinate descent optimizer with

   Newton update, active sets, update caching, and warm restarts. This

   optimization approach is identical to the one used in R package.

-  A number of Python wrappers exist for the R glmnet package (e.g.

   `here `__ and

   `here `__) but in contrast to

   these, Pyglmnet is a pure python implementation. Therefore, it is

   easy to modify and introduce additional noise models and regularizers

   in the future.

Installation

~~~~~~~~~~~~

Install the stable PyPI version with ``pip``

.. code:: bash

    $ pip install pyglmnet

For the bleeding edge development version:

Clone the repository.

.. code:: bash

    $ pip install https://api.github.com/repos/glm-tools/pyglmnet/zipball/master

Getting Started

~~~~~~~~~~~~~~~

Here is an example on how to use the ``GLM`` estimator.

.. code:: python

    import numpy as np

    import scipy.sparse as sps

    import matplotlib.pyplot as plt

    from pyglmnet import GLM, simulate_glm

    n_samples, n_features = 1000, 100

    distr = 'poisson'

    # sample a sparse model

    np.random.seed(42)

    beta0 = np.random.rand()

    beta = sps.random(1, n_features, density=0.2).toarray()[0]

    # simulate data

    Xtrain = np.random.normal(0.0, 1.0, [n_samples, n_features])

    ytrain = simulate_glm('poisson', beta0, beta, Xtrain)

    Xtest = np.random.normal(0.0, 1.0, [n_samples, n_features])

    ytest = simulate_glm('poisson', beta0, beta, Xtest)

    # create an instance of the GLM class

    glm = GLM(distr='poisson', score_metric='pseudo_R2', reg_lambda=0.01)

    # fit the model on the training data

    glm.fit(Xtrain, ytrain)

    # predict using fitted model on the test data

    yhat = glm.predict(Xtest)

    # score the model on test data

    pseudo_R2 = glm.score(Xtest, ytest)

    print('Pseudo R^2 is %.3f' % pseudo_R2)

    # plot the true coefficients and the estimated ones

    plt.stem(beta, markerfmt='r.', label='True coefficients')

    plt.stem(glm.beta_, markerfmt='b.', label='Estimated coefficients')

    plt.ylabel(r'$\beta$')

    plt.legend(loc='upper right')

    # plot the true vs predicted label

    plt.figure()

    plt.plot(ytest, yhat, '.')

    plt.xlabel('True labels')

    plt.ylabel('Predicted labels')

    plt.plot([0, ytest.max()], [0, ytest.max()], 'r--')

    plt.show()

`More pyglmnet examples and use

cases `__.

Tutorial

~~~~~~~~

Here is an `extensive

tutorial `__ on GLMs,

optimization and pseudo-code.

Here are

`slides `__ from a

talk at `PyData Chicago

2016 `__,

corresponding `tutorial

notebooks `__ and a

`video `__.

How to contribute?

~~~~~~~~~~~~~~~~~~

We welcome pull requests. Please see our `developer documentation

page `__ for more

details.

Citation

~~~~~~~~

If you use ``pyglmnet`` package in your publication, please cite us from

our `JOSS publication `__ using the following BibTex

.. code::

   @article{Jas2020,

   doi = {10.21105/joss.01959},

   url = {https://doi.org/10.21105/joss.01959},

   year = {2020},

   publisher = {The Open Journal},

   volume = {5},

   number = {47},

   pages = {1959},

   author = {Mainak Jas and Titipat Achakulvisut and Aid Idrizović

             and Daniel Acuna and Matthew Antalek and Vinicius Marques

             and Tommy Odland and Ravi Garg and Mayank Agrawal

             and Yu Umegaki and Peter Foley and Hugo Fernandes

             and Drew Harris and Beibin Li and Olivier Pieters

             and Scott Otterson and Giovanni De Toni and Chris Rodgers

             and Eva Dyer and Matti Hamalainen and Konrad Kording and Pavan Ramkumar},

   title = {{P}yglmnet: {P}ython implementation of elastic-net regularized generalized linear models},

   journal = {Journal of Open Source Software}

   }

Acknowledgments

~~~~~~~~~~~~~~~

-  `Konrad Kording `__ for funding and support

-  `Sara

   Solla `__

   for masterful GLM lectures

License

~~~~~~~

MIT License Copyright (c) 2016-2019 Pavan Ramkumar

.. |License| image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat

   :target: https://github.com/glm-tools/pyglmnet/blob/master/LICENSE

.. |Travis| image:: https://api.travis-ci.org/glm-tools/pyglmnet.svg?branch=master

   :target: https://travis-ci.org/glm-tools/pyglmnet

.. |Codecov| image:: https://codecov.io/github/glm-tools/pyglmnet/coverage.svg?precision=0

   :target: https://codecov.io/gh/glm-tools/pyglmnet

.. |Circle| image:: https://circleci.com/gh/glm-tools/pyglmnet.svg?style=svg

   :target: https://circleci.com/gh/glm-tools/pyglmnet

.. |Gitter| image:: https://badges.gitter.im/glm-tools/pyglmnet.svg

   :target: https://gitter.im/pavanramkumar/pyglmnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge

.. |DOI| image:: https://zenodo.org/badge/55302570.svg

   :target: https://zenodo.org/badge/latestdoi/55302570

.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.01959/status.svg

   :target: https://doi.org/10.21105/joss.01959

.. _[Documentation (stable version)]: http://glm-tools.github.io/pyglmnet