Using pip
pip install glmnetforpythonFrom GitHub
pip install git+github.com/thierrymouidiki/glmnetforpython.gitThis 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.
See:
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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
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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
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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.