Python implementation of elastic-net regularized generalized linear models
pavanramkumar_pyglmnet
pavanramkumar
Overview
Abstract
pyglmnet
========
A python implementation of elastic-net regularized generalized linear models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|License| |Travis| |Codecov| |Circle| |Gitter| |DOI|
`[Documentation (stable version)]`_ `[Documentation (development version)]`_
.. raw:: html
<img src="pyglmnet-logo.png" height="400px">
`Generalized linear
models <https://en.wikipedia.org/wiki/Generalized_linear_model>`__ are
well-established tools for regression and classification and are widely
applied across the sciences, economics, business, and finance. They are
uniquely identifiable due to their convex loss and easy to interpret due
to their point-wise non-linearities and well-defined noise models.
In the era of exploratory data analyses with a large number of predictor
variables, it is important to regularize. Regularization prevents
overfitting by penalizing the negative log likelihood and can be used to
articulate prior knowledge about the parameters in a structured form.
Despite the attractiveness of regularized GLMs, the available tools in
the Python data science eco-system are highly fragmented. More
specifically,
- `statsmodels <http://statsmodels.sourceforge.net/devel/glm.html>`__
provides a wide range of link functions but no regularization.
- `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`__
provides elastic net regularization but only for linear models.
- `lightning <https://github.com/scikit-learn-contrib/lightning>`__
provides elastic net and group lasso regularization, but only for
linear and logistic regression.
**Pyglmnet** is a response to this fragmentation. It runs on Python 3.5+,
and here are some of the highlights.
- 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 <https://en.wikipedia.org/wiki/Proximal_gradient_methods_for_learning#Group_lasso>`__,
and `Tikhonov
regularization <https://en.wikipedia.org/wiki/Tikhonov_regularization>`__.
- Pyglmnet's API is designed to be compatible with scikit-learn, so you
can deploy ``Pipeline`` tools such as ``GridSearchCV()`` and
``cross_val_score()``.
- We follow the same approach and notations as in `Friedman, J.,
Hastie, T., & Tibshirani, R.
(2010) <https://core.ac.uk/download/files/153/6287975.pdf>`__ and the
accompanying widely popular `R
package <https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
- 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 <https://github.com/civisanalytics/python-glmnet>`__ and
`here <https://github.com/dwf/glmnet-python>`__) 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
from pyglmnet import GLM, simulate_glm
n_samples, n_features = 1000, 100
distr = 'poisson'
# sample a sparse model
beta0 = np.random.rand()
beta = np.random.random(n_features)
beta[beta < 0.9] = 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='deviance')
# 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
deviance = glm.score(Xtest, ytest)
`More pyglmnet examples and use
cases <http://glm-tools.github.io/pyglmnet/auto_examples/index.html>`__.
Tutorial
~~~~~~~~
Here is an `extensive
tutorial <http://glm-tools.github.io/pyglmnet/tutorial.html>`__ on GLMs,
optimization and pseudo-code.
Here are
`slides <https://pavanramkumar.github.io/pydata-chicago-2016>`__ from a
talk at `PyData Chicago
2016 <http://pydata.org/chicago2016/schedule/presentation/15/>`__,
corresponding `tutorial
notebooks <http://github.com/pavanramkumar/pydata-chicago-2016>`__ and a
`video <https://www.youtube.com/watch?v=zXec96KD1uA>`__.
How to contribute?
~~~~~~~~~~~~~~~~~~
We welcome pull requests. Please see our `developer documentation
page <http://glm-tools.github.io/pyglmnet/developers.html>`__ for more
details.
Acknowledgments
~~~~~~~~~~~~~~~
- `Konrad Kording <http://kordinglab.com>`__ for funding and support
- `Sara
Solla <http://www.physics.northwestern.edu/people/joint-faculty/sara-solla.html>`__
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
.. _[Documentation (stable version)]: http://glm-tools.github.io/pyglmnet
.. _[Documentation (development version)]: https://circleci.com/api/v1.1/project/github/glm-tools/pyglmnet/latest/artifacts/0/html/index.html?branch=master
========
A python implementation of elastic-net regularized generalized linear models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|License| |Travis| |Codecov| |Circle| |Gitter| |DOI|
`[Documentation (stable version)]`_ `[Documentation (development version)]`_
.. raw:: html
<img src="pyglmnet-logo.png" height="400px">
`Generalized linear
models <https://en.wikipedia.org/wiki/Generalized_linear_model>`__ are
well-established tools for regression and classification and are widely
applied across the sciences, economics, business, and finance. They are
uniquely identifiable due to their convex loss and easy to interpret due
to their point-wise non-linearities and well-defined noise models.
In the era of exploratory data analyses with a large number of predictor
variables, it is important to regularize. Regularization prevents
overfitting by penalizing the negative log likelihood and can be used to
articulate prior knowledge about the parameters in a structured form.
Despite the attractiveness of regularized GLMs, the available tools in
the Python data science eco-system are highly fragmented. More
specifically,
- `statsmodels <http://statsmodels.sourceforge.net/devel/glm.html>`__
provides a wide range of link functions but no regularization.
- `scikit-learn <http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html>`__
provides elastic net regularization but only for linear models.
- `lightning <https://github.com/scikit-learn-contrib/lightning>`__
provides elastic net and group lasso regularization, but only for
linear and logistic regression.
**Pyglmnet** is a response to this fragmentation. It runs on Python 3.5+,
and here are some of the highlights.
- 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 <https://en.wikipedia.org/wiki/Proximal_gradient_methods_for_learning#Group_lasso>`__,
and `Tikhonov
regularization <https://en.wikipedia.org/wiki/Tikhonov_regularization>`__.
- Pyglmnet's API is designed to be compatible with scikit-learn, so you
can deploy ``Pipeline`` tools such as ``GridSearchCV()`` and
``cross_val_score()``.
- We follow the same approach and notations as in `Friedman, J.,
Hastie, T., & Tibshirani, R.
(2010) <https://core.ac.uk/download/files/153/6287975.pdf>`__ and the
accompanying widely popular `R
package <https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html>`__.
- 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 <https://github.com/civisanalytics/python-glmnet>`__ and
`here <https://github.com/dwf/glmnet-python>`__) 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
from pyglmnet import GLM, simulate_glm
n_samples, n_features = 1000, 100
distr = 'poisson'
# sample a sparse model
beta0 = np.random.rand()
beta = np.random.random(n_features)
beta[beta < 0.9] = 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='deviance')
# 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
deviance = glm.score(Xtest, ytest)
`More pyglmnet examples and use
cases <http://glm-tools.github.io/pyglmnet/auto_examples/index.html>`__.
Tutorial
~~~~~~~~
Here is an `extensive
tutorial <http://glm-tools.github.io/pyglmnet/tutorial.html>`__ on GLMs,
optimization and pseudo-code.
Here are
`slides <https://pavanramkumar.github.io/pydata-chicago-2016>`__ from a
talk at `PyData Chicago
2016 <http://pydata.org/chicago2016/schedule/presentation/15/>`__,
corresponding `tutorial
notebooks <http://github.com/pavanramkumar/pydata-chicago-2016>`__ and a
`video <https://www.youtube.com/watch?v=zXec96KD1uA>`__.
How to contribute?
~~~~~~~~~~~~~~~~~~
We welcome pull requests. Please see our `developer documentation
page <http://glm-tools.github.io/pyglmnet/developers.html>`__ for more
details.
Acknowledgments
~~~~~~~~~~~~~~~
- `Konrad Kording <http://kordinglab.com>`__ for funding and support
- `Sara
Solla <http://www.physics.northwestern.edu/people/joint-faculty/sara-solla.html>`__
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
.. _[Documentation (stable version)]: http://glm-tools.github.io/pyglmnet
.. _[Documentation (development version)]: https://circleci.com/api/v1.1/project/github/glm-tools/pyglmnet/latest/artifacts/0/html/index.html?branch=master
Authoring entity
| Agency Name | Role | Affiliation |
|---|---|---|
| pavanramkumar | owner | http://pavanramkumar.github.io/pyglmnet/ |
Language
English
Methods, software and scripts
Software
| Name |
|---|
| Makefile |
| Python |
License
| Name |
|---|
| MIT License |
Metadata production
Producers
| Name | Role |
|---|---|
| GitHub Bot | bot |
Date of Production
01 December 2019