Pyglmnet

Pyglmnet: Python implementation of elastic-net regularized generalized linear models - Published in JOSS (2020)

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

Science Score: 95.0%

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Found 9 DOI reference(s) in README and JOSS metadata
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Published in Journal of Open Source Software

Keywords

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

Scientific Fields

Mathematics Computer Science - 84% confidence

Last synced: 4 months ago · JSON representation

Repository

Python implementation of elastic-net regularized generalized linear models

Basic Info

Host: GitHub
Owner: glm-tools
License: mit
Language: Python
Default Branch: master
Homepage: http://glm-tools.github.io/pyglmnet/
Size: 7.31 MB

Statistics

Stars: 284
Watchers: 12
Forks: 84
Open Issues: 39
Releases: 3

Topics

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

Created over 9 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Zenodo

README.rst

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

Owner

Name: glm-tools
Login: glm-tools
Kind: organization

Repositories: 2
Profile: https://github.com/glm-tools

JOSS Publication

Pyglmnet: Python implementation of elastic-net regularized generalized linear models

Published

March 01, 2020

DOI

10.21105/joss.01959

Volume 5, Issue 47, Page 1959

Authors

Mainak Jas

Massachusetts General Hospital, Harvard Medical School

Titipat Achakulvisut

University of Pennsylvania

Aid Idrizović
Loyola University

Daniel Acuna
University of Syracuse

Matthew Antalek
Northwestern University

Vinicius Marques
Loyola University

Tommy Odland
Sonat Consulting

Ravi Prakash Garg
Northwestern University

Mayank Agrawal
Princeton University

Yu Umegaki
NTT DATA Mathematical Systems Inc

Peter Foley

605

Hugo Fernandes

Rockets of Awesome

Drew Harris
Epoch Capital

Beibin Li
University of Washington

Olivier Pieters

IDLab-AIRO -- Ghent University -- imec, Research Institute for Agriculture, Fisheries and Food

Scott Otterson
Clean Power Research

Giovanni De Toni

University of Trento

Chris Rodgers

Columbia University

Eva Dyer
Georgia Tech

Matti Hamalainen
Massachusetts General Hospital, Harvard Medical School

Konrad Kording
University of Pennsylvania

Pavan Ramkumar

System1 Biosciences Inc

Editor

Ariel Rokem

Papers & Mentions

Total mentions: 2

Active learning of cortical connectivity from two-photon imaging data

DOI: 10.1371/journal.pone.0196527
OpenAlex ID: https://openalex.org/W2949183692
Published: May 2018

Last synced: 3 months ago

Modern Machine Learning as a Benchmark for Fitting Neural Responses

DOI: 10.3389/fncom.2018.00056
OpenAlex ID: https://openalex.org/W2810603154
Published: July 2018

Last synced: 3 months ago

GitHub Events

Total

Watch event: 6
Fork event: 1

Last Year

Watch event: 6
Fork event: 1

Committers

Last synced: 5 months ago

All Time

Total Commits: 736
Total Committers: 29
Avg Commits per committer: 25.379
Development Distribution Score (DDS): 0.598

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Pavan Ramkumar	p**r@g**m	296
Mainak Jas	m**s@g**m	177
Titipat Achakulvisut	m**t@g**m	59
Giovanni De Toni	g**t@g**m	37
titipata	t**a@u**u	28
Aid Idrizović	i**d@g**m	17
Matthew Antalek	m**k@g**m	16
Daniel E. Acuna	d**a@g**m	15
Vinicius de F. Marques	v**e@g**m	14
Tommy Odland	t**d@g**m	13
Mayank Agrawal	m**6@g**m	9
Ravi Prakash Garg	r**7@g**m	9
Peter Foley	p**y@g**m	7
Yu Umegaki	y**i@g**m	7
Hugo Fernandes	h**h@g**m	4
Olivier Pieters	o**s@u**e	4
Hugo L Fernandes	h**s@n**u	4
Thomas Kunwar	y****i	3
Beibin Li	B****i	2
Chris Rodgers	x**s@g**m	2
Drew Harris	d**z@g**m	2
Pattarawat Chormai	p**i@g**m	2
Scott Otterson	s**o@s**g	2
Talles Alves	t**v@l**u	2
Eva Dyer	e**h@g**m	1
Ilias Tapeinos	I**9@g**m	1
Yu Umegaki	u**i@m**p	1
Zan Markan	z**n@m**e	1
debroize	4****e	1

Committer Domains (Top 20 + Academic)

markan.me: 1 msi.co.jp: 1 luc.edu: 1 sharpleaf.org: 1 northwestern.edu: 1 ugent.be: 1 u.northwestern.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 49
Total pull requests: 53
Average time to close issues: 9 months
Average time to close pull requests: 19 days
Total issue authors: 25
Total pull request authors: 12
Average comments per issue: 3.49
Average comments per pull request: 5.68
Merged pull requests: 42
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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pavanramkumar (12)
jasmainak (10)
titipata (3)
cxrodgers (2)
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jpainam (1)
cyradil (1)
AnchorBlues (1)
ivarzap (1)
themantalope (1)
idc9 (1)
ravigarg27 (1)
cruyffturn (1)

Pull Request Authors

jasmainak (17)
pavanramkumar (9)
titipata (8)
yathomasi (6)
geektoni (5)
p16i (2)
timshell (1)
opieters (1)
zmarkan (1)
AnchorBlues (1)
ita07 (1)

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Issue Labels

MAINT (9) enhancement (6) easy (6) docs (5) bug (3) API (1) tests (1)

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docs (1)

Packages

Total packages: 2
Total downloads:
- pypi 2,295 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 5
(may contain duplicates)
Total versions: 3
Total maintainers: 2

pypi.org: pyglmnet

Elastic-net regularized generalized linear models.

Homepage: http://glm-tools.github.io/pyglmnet/
Documentation: http://glm-tools.github.io/pyglmnet/
License: MIT
Latest release: 1.0.0
published about 9 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 5
Downloads: 2,295 Last month
Docker Downloads: 0

Rankings

Docker downloads count: 3.7%

Stargazers count: 3.9%

Forks count: 4.9%

Average: 6.0%

Dependent repos count: 6.6%

Downloads: 6.6%

Dependent packages count: 10.1%

Maintainers (2)

mainakjas glm-tools

Last synced: 4 months ago

conda-forge.org: pyglmnet

Homepage: https://github.com/glm-tools/pyglmnet
License: MIT
Latest release: 1.1
published almost 6 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Forks count: 18.9%

Stargazers count: 21.7%

Average: 31.5%

Dependent repos count: 34.0%

Dependent packages count: 51.2%

Last synced: 4 months ago

Dependencies

requirements.txt pypi

coverage *
flake8 *
numpy >=1.11
pandas >=0.20
pydocstyle *
pytest *
pytest-cov *
scikit-learn >=0.18
scipy >=0.17
tqdm >=4.46

Pyglmnet

Science Score: 95.0%

Keywords

Scientific Fields

Repository

Basic Info

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Metadata Files

README.rst

Owner

JOSS Publication

Pyglmnet: Python implementation of elastic-net regularized generalized linear models

Authors

Editor

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Active learning of cortical connectivity from two-photon imaging data

Modern Machine Learning as a Benchmark for Fitting Neural Responses

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