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@@ -0,0 +1 @@ +/glmnet_py-0.1.0b2.tar.gz diff --git a/python-glmnet-py.spec b/python-glmnet-py.spec new file mode 100644 index 0000000..9e1e89a --- /dev/null +++ b/python-glmnet-py.spec @@ -0,0 +1,316 @@ +%global _empty_manifest_terminate_build 0 +Name: python-glmnet-py +Version: 0.1.0b2 +Release: 1 +Summary: Python version of glmnet, originally from Stanford University, modified by Han Fang +License: GPL-2 +URL: https://github.com/hanfang/glmnet_py +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f5/1a/55b708789a66f2405783bce0f53e02b86128ea3f21d329c87dc6dbef8e51/glmnet_py-0.1.0b2.tar.gz +BuildArch: noarch + +Requires: python3-joblib + +%description +# Glmnet for python + +## Contact + +Han Fang +hanfang.cshl@gmail.com + +## Install + +Using pip (recommended) + + pip install glmnet_py + +Complied from source + + git clone https://github.com/hanfang/glmnet_py.git + cd glmnet_py + python setup.py install + +Requirement: Python3, 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) + + +## Usage + import glmnet_py + from glmnet import glmnet + +For more examples, see https://github.com/hanfang/glmnet_python/tree/master/test + + +## Introduction + +This is a python version of the popular `glmnet` library (beta release). 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. + +CV can be done in both serial and parallel manner. Parallellization is done using `multiprocessing` and `joblib` libraries. + +During installation, the fortran code is compiled in the local machine using `gfortran`, and is called by the python code. + +````diff ++Getting started: +```` +*The best starting point to use this library is to start with the Jupyter notebooks in the `test` directory (glmnet_examples.ipynb). Detailed explanations of function calls and parameter values along with plenty of examples are provided there to get you started.* + +## Authors: + +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), later modified by Han Fang. + +B. J. Balakumar, bbalasub@stanford.edu (5 Sep 2016). +Department of Statistics, Stanford University, Stanford, California, USA. + +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* + + + + + +%package -n python3-glmnet-py +Summary: Python version of glmnet, originally from Stanford University, modified by Han Fang +Provides: python-glmnet-py +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-glmnet-py +# Glmnet for python + +## Contact + +Han Fang +hanfang.cshl@gmail.com + +## Install + +Using pip (recommended) + + pip install glmnet_py + +Complied from source + + git clone https://github.com/hanfang/glmnet_py.git + cd glmnet_py + python setup.py install + +Requirement: Python3, 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) + + +## Usage + import glmnet_py + from glmnet import glmnet + +For more examples, see https://github.com/hanfang/glmnet_python/tree/master/test + + +## Introduction + +This is a python version of the popular `glmnet` library (beta release). 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. + +CV can be done in both serial and parallel manner. Parallellization is done using `multiprocessing` and `joblib` libraries. + +During installation, the fortran code is compiled in the local machine using `gfortran`, and is called by the python code. + +````diff ++Getting started: +```` +*The best starting point to use this library is to start with the Jupyter notebooks in the `test` directory (glmnet_examples.ipynb). Detailed explanations of function calls and parameter values along with plenty of examples are provided there to get you started.* + +## Authors: + +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), later modified by Han Fang. + +B. J. Balakumar, bbalasub@stanford.edu (5 Sep 2016). +Department of Statistics, Stanford University, Stanford, California, USA. + +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* + + + + + +%package help +Summary: Development documents and examples for glmnet-py +Provides: python3-glmnet-py-doc +%description help +# Glmnet for python + +## Contact + +Han Fang +hanfang.cshl@gmail.com + +## Install + +Using pip (recommended) + + pip install glmnet_py + +Complied from source + + git clone https://github.com/hanfang/glmnet_py.git + cd glmnet_py + python setup.py install + +Requirement: Python3, 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) + + +## Usage + import glmnet_py + from glmnet import glmnet + +For more examples, see https://github.com/hanfang/glmnet_python/tree/master/test + + +## Introduction + +This is a python version of the popular `glmnet` library (beta release). 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. + +CV can be done in both serial and parallel manner. Parallellization is done using `multiprocessing` and `joblib` libraries. + +During installation, the fortran code is compiled in the local machine using `gfortran`, and is called by the python code. + +````diff ++Getting started: +```` +*The best starting point to use this library is to start with the Jupyter notebooks in the `test` directory (glmnet_examples.ipynb). Detailed explanations of function calls and parameter values along with plenty of examples are provided there to get you started.* + +## Authors: + +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), later modified by Han Fang. + +B. J. Balakumar, bbalasub@stanford.edu (5 Sep 2016). +Department of Statistics, Stanford University, Stanford, California, USA. + +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* + + + + + +%prep +%autosetup -n glmnet-py-0.1.0b2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-glmnet-py -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0b2-1 +- Package Spec generated @@ -0,0 +1 @@ +887f736fff59f9568b86cc5044727687 glmnet_py-0.1.0b2.tar.gz |