Installation ~~~~

.. 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 ~~~

.. code:: python

import numpy as np import scipy.sparse as sps

import matplotlib.pyplot as plt from pyglmnet import GLM, simulate_glm

nsamples, nfeatures = 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, [nsamples, nfeatures]) ytrain = simulate_glm('poisson', beta0, beta, Xtrain) Xtest = np.random.normal(0.0, 1.0, [nsamples, nfeatures]) ytest = simulate_glm('poisson', beta0, beta, Xtest)

# create an instance of the GLM class glm = GLM(distr='poisson', scoremetric='pseudoR2', 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()

Tutorial ~~~~

How to contribute? ~~~~~~

Citation ~~~~

.. 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 ~~~