Glmnet for python

Install

Using pip

pip install glmnetforpython

From GitHub

pip install git+github.com/thierrymouidiki/glmnetforpython.git

What?

This is a python version of the popular glmnet library (scikit-learn style). Glmnet fits the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the cox model.

The underlying fortran codes are the same as the R version, and uses a cyclical path-wise coordinate descent algorithm as described in the papers linked below.

Currently, glmnet library methods for gaussian, multi-variate gaussian, binomial, multinomial, poisson and cox models are implemented for both normal and sparse matrices.

Additionally, cross-validation is also implemented for gaussian, multivariate gaussian, binomial, multinomial and poisson models. CV for cox models is yet to be implemented.

Usage

See:

• regression examples
• classification examples

References:

• Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, http://www.jstatsoft.org/v33/i01/ Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010

• Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, http://www.jstatsoft.org/v39/i05/ Journal of Statistical Software, Vol. 39(5) 1-13

• Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2010) Strong Rules for Discarding Predictors in Lasso-type Problems, http://www-stat.stanford.edu/~tibs/ftp/strong.pdf Stanford Statistics Technical Report

License:

This software is released under GNU General Public License v3.0 or later.