[Documentation (stable version)] `[Documentation (development version)]`_
- 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.
Install the stable PyPI version with pip
$ pip install pyglmnetFor the bleeding edge development version:
Clone the repository.
$ pip install https://api.github.com/repos/glm-tools/pyglmnet/zipball/masterHere is an example on how to use the GLM estimator.
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.
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.
We welcome pull requests. Please see our developer documentation page for more details.
If you use pyglmnet package in your publication, please cite us from
our JOSS publication using the following BibTex
@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}
}
- Konrad Kording for funding and support
- Sara Solla for masterful GLM lectures
MIT License Copyright (c) 2016-2019 Pavan Ramkumar