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@@ -0,0 +1 @@ +/gglasso-0.1.9.tar.gz diff --git a/python-gglasso.spec b/python-gglasso.spec new file mode 100644 index 0000000..286c896 --- /dev/null +++ b/python-gglasso.spec @@ -0,0 +1,396 @@ +%global _empty_manifest_terminate_build 0 +Name: python-gglasso +Version: 0.1.9 +Release: 1 +Summary: Algorithms for Single and Multiple Graphical Lasso problems. +License: MIT +URL: https://github.com/fabian-sp/GGLasso +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1f/b9/5ac04eaf3cd77d04acf5c00b832cb5787208ee744697159e5afdff202d47/gglasso-0.1.9.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-numba +Requires: python3-pandas +Requires: python3-matplotlib +Requires: python3-seaborn +Requires: python3-networkx +Requires: python3-regain +Requires: python3-decorator +Requires: python3-sphinx +Requires: python3-sphinx-gallery +Requires: python3-sphinx-rtd-theme +Requires: python3-pytest +Requires: python3-pytest-cov + +%description + +# GGLasso + +[](https://pypi.python.org/pypi/gglasso/) +[](https://pypi.python.org/pypi/gglasso/) +[](https://www.python.org/) +[](http://gglasso.readthedocs.io/?badge=latest) +[](https://doi.org/10.21105/joss.03865) +[](https://arxiv.org/abs/2110.10521) + + +This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent +Graphical Lasso problems. <br> + +[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html) + +## Getting started + +### Install via pip + +The package is available on pip and can be installed with + + pip install gglasso + +### Install from source + +Alternatively, you can install the package from source using the following commands: + + git clone https://github.com/fabian-sp/GGLasso.git + pip install -r requirements.txt + python setup.py + +Test your installation with + + pytest gglasso/ -v + + +### Advanced options + +When installing from source, you can also install dependencies with `conda` via the command + + $ while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt + +If you wish to install `gglasso` in developer mode, i.e. not having to reinstall `gglasso` everytime the source code changes (either by remote or local changes), run + + python setup.py clean --all develop clean --all + +## The `glasso_problem` class + +`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details. + + +## Algorithms + +`GGLasso` contains algorithms for Single and Multiple Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type **sparse - low rank**. The following algorithms are contained in the package. +<br> +1) ADMM for Single Graphical Lasso<br> + +2) ADMM for Group and Fused Graphical Lasso<br> +The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.<br> + +3) A Proximal Point method for Group and Fused Graphical Lasso<br> +We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br> + +4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming<br> +Method for problems where not all variables exist in all instances/datasets. To use this, import `ext_ADMM_MGL` from `gglasso/solver/ext_admm_solver`.<br> + +## Citation + +If you use `GGLasso`, please consider the following citation + + @article{Schaipp2021, + doi = {10.21105/joss.03865}, + url = {https://doi.org/10.21105/joss.03865}, + year = {2021}, + publisher = {The Open Journal}, + volume = {6}, + number = {68}, + pages = {3865}, + author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller}, + title = {GGLasso - a Python package for General Graphical Lasso computation}, + journal = {Journal of Open Source Software} + } + + +## Community Guidelines + +1) Contributions and suggestions to the software are always welcome. + Please, consult our [contribution guidelines](CONTRIBUTING.md) prior + to submitting a pull request. +2) Report issues or problems with the software using github’s [issue + tracker](https://github.com/fabian-sp/GGLasso/issues). +3) Contributors must adhere to the [Code of + Conduct](CODE_OF_CONDUCT.md). + + +## References +* [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007). Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441. +* [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397. +* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM. +* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220. + + + + +%package -n python3-gglasso +Summary: Algorithms for Single and Multiple Graphical Lasso problems. +Provides: python-gglasso +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-gglasso + +# GGLasso + +[](https://pypi.python.org/pypi/gglasso/) +[](https://pypi.python.org/pypi/gglasso/) +[](https://www.python.org/) +[](http://gglasso.readthedocs.io/?badge=latest) +[](https://doi.org/10.21105/joss.03865) +[](https://arxiv.org/abs/2110.10521) + + +This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent +Graphical Lasso problems. <br> + +[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html) + +## Getting started + +### Install via pip + +The package is available on pip and can be installed with + + pip install gglasso + +### Install from source + +Alternatively, you can install the package from source using the following commands: + + git clone https://github.com/fabian-sp/GGLasso.git + pip install -r requirements.txt + python setup.py + +Test your installation with + + pytest gglasso/ -v + + +### Advanced options + +When installing from source, you can also install dependencies with `conda` via the command + + $ while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt + +If you wish to install `gglasso` in developer mode, i.e. not having to reinstall `gglasso` everytime the source code changes (either by remote or local changes), run + + python setup.py clean --all develop clean --all + +## The `glasso_problem` class + +`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details. + + +## Algorithms + +`GGLasso` contains algorithms for Single and Multiple Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type **sparse - low rank**. The following algorithms are contained in the package. +<br> +1) ADMM for Single Graphical Lasso<br> + +2) ADMM for Group and Fused Graphical Lasso<br> +The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.<br> + +3) A Proximal Point method for Group and Fused Graphical Lasso<br> +We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br> + +4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming<br> +Method for problems where not all variables exist in all instances/datasets. To use this, import `ext_ADMM_MGL` from `gglasso/solver/ext_admm_solver`.<br> + +## Citation + +If you use `GGLasso`, please consider the following citation + + @article{Schaipp2021, + doi = {10.21105/joss.03865}, + url = {https://doi.org/10.21105/joss.03865}, + year = {2021}, + publisher = {The Open Journal}, + volume = {6}, + number = {68}, + pages = {3865}, + author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller}, + title = {GGLasso - a Python package for General Graphical Lasso computation}, + journal = {Journal of Open Source Software} + } + + +## Community Guidelines + +1) Contributions and suggestions to the software are always welcome. + Please, consult our [contribution guidelines](CONTRIBUTING.md) prior + to submitting a pull request. +2) Report issues or problems with the software using github’s [issue + tracker](https://github.com/fabian-sp/GGLasso/issues). +3) Contributors must adhere to the [Code of + Conduct](CODE_OF_CONDUCT.md). + + +## References +* [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007). Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441. +* [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397. +* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM. +* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220. + + + + +%package help +Summary: Development documents and examples for gglasso +Provides: python3-gglasso-doc +%description help + +# GGLasso + +[](https://pypi.python.org/pypi/gglasso/) +[](https://pypi.python.org/pypi/gglasso/) +[](https://www.python.org/) +[](http://gglasso.readthedocs.io/?badge=latest) +[](https://doi.org/10.21105/joss.03865) +[](https://arxiv.org/abs/2110.10521) + + +This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent +Graphical Lasso problems. <br> + +[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html) + +## Getting started + +### Install via pip + +The package is available on pip and can be installed with + + pip install gglasso + +### Install from source + +Alternatively, you can install the package from source using the following commands: + + git clone https://github.com/fabian-sp/GGLasso.git + pip install -r requirements.txt + python setup.py + +Test your installation with + + pytest gglasso/ -v + + +### Advanced options + +When installing from source, you can also install dependencies with `conda` via the command + + $ while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt + +If you wish to install `gglasso` in developer mode, i.e. not having to reinstall `gglasso` everytime the source code changes (either by remote or local changes), run + + python setup.py clean --all develop clean --all + +## The `glasso_problem` class + +`GGLasso` can solve multiple problem forumulations, e.g. single and multiple Graphical Lasso problems as well as with and without latent factors. Therefore, the main entry point for the user is the `glasso_problem` class which chooses automatically the correct solver and model selection functionality. See [our documentation](https://gglasso.readthedocs.io/en/latest/problem-object.html) for all the details. + + +## Algorithms + +`GGLasso` contains algorithms for Single and Multiple Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type **sparse - low rank**. The following algorithms are contained in the package. +<br> +1) ADMM for Single Graphical Lasso<br> + +2) ADMM for Group and Fused Graphical Lasso<br> +The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.<br> + +3) A Proximal Point method for Group and Fused Graphical Lasso<br> +We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br> + +4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming<br> +Method for problems where not all variables exist in all instances/datasets. To use this, import `ext_ADMM_MGL` from `gglasso/solver/ext_admm_solver`.<br> + +## Citation + +If you use `GGLasso`, please consider the following citation + + @article{Schaipp2021, + doi = {10.21105/joss.03865}, + url = {https://doi.org/10.21105/joss.03865}, + year = {2021}, + publisher = {The Open Journal}, + volume = {6}, + number = {68}, + pages = {3865}, + author = {Fabian Schaipp and Oleg Vlasovets and Christian L. Müller}, + title = {GGLasso - a Python package for General Graphical Lasso computation}, + journal = {Journal of Open Source Software} + } + + +## Community Guidelines + +1) Contributions and suggestions to the software are always welcome. + Please, consult our [contribution guidelines](CONTRIBUTING.md) prior + to submitting a pull request. +2) Report issues or problems with the software using github’s [issue + tracker](https://github.com/fabian-sp/GGLasso/issues). +3) Contributors must adhere to the [Code of + Conduct](CODE_OF_CONDUCT.md). + + +## References +* [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007). Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432–441. +* [2] Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2):373–397. +* [3] Tomasi, F., Tozzo, V., Salzo, S., and Verri, A. (2018). Latent Variable Time-varying Network Inference. InProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM. +* [4] Zhang, Y., Zhang, N., Sun, D., and Toh, K.-C. (2020). A proximal point dual Newton algorithm for solving group graphical Lasso problems. SIAM J. Optim., 30(3):2197–2220. + + + + +%prep +%autosetup -n gglasso-0.1.9 + +%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-gglasso -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.9-1 +- Package Spec generated @@ -0,0 +1 @@ +066943773c9e6b050a2a6c3c9001e675 gglasso-0.1.9.tar.gz |