Glmnet for python
Using pip (recommended)
pip install glmnet_py
Complied from source
git clone https://github.com/bbalasub1/glmnet_python.git cd glmnet_python python setup.py install (use python setup.py install --user if you get a permission denied message. This does a local install for the user)
Requirement: Python 3, Linux
Currently, the checked-in version of GLMnet.so is compiled for the following config:
Linux: Linux version 2.6.32-573.26.1.el6.x86_64 (gcc version 4.4.7 20120313 (Red Hat 4.4.7-16) (GCC) ) OS: CentOS 6.7 (Final) Hardware: 8-core Intel(R) Core(TM) i7-2630QM gfortran: version 4.4.7 20120313 (Red Hat 4.4.7-17) (GCC)
For MacOS installation, here are some solutions that have worked for others: https://github.com/bbalasub1/glmnet_python/issues/13#issuecomment-813000987 **
Read the Docs: or click me
import glmnet_python from glmnet import glmnet
For more examples, see iPython notebook
This is a python version of the popular
glmnet library (beta release). Glmnet fits the entire lasso or elastic-net regularization path for
multinomial regression models,
poisson regression and the
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.
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.
CV can be done in both serial and parallel manner. Parallellization is done using
During installation, the fortran code is compiled in the local machine using
gfortran, and is called by the python code.
The best starting point to use this library is to start with the Jupyter notebooks in the
test directory (iPython notebook). Detailed explanations of function calls and parameter values along with plenty of examples are provided there to get you started.
Algorithm was designed by Jerome Friedman, Trevor Hastie and Rob Tibshirani. Fortran code was written by Jerome Friedman. R wrapper (from which the MATLAB wrapper was adapted) was written by Trevor Hastie.
The original MATLAB wrapper was written by Hui Jiang (14 Jul 2009), and was updated and is maintained by Junyang Qian (30 Aug 2013).
This python wrapper (which was adapted from the MATLAB and R wrappers) was originally written by B. J. Balakumar (5 Sep 2016).
List of other contributors along with a summary of their contributions is included in the contributors.dat file.
B. J. Balakumar, firstname.lastname@example.org (Sep 5, 2016). Department of Statistics, Stanford University, Stanford, CA
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
This software is released under GNU General Public License v3.0 or later.