%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.
[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.
1) ADMM for Single Graphical Lasso
2) ADMM for Group and Fused Graphical Lasso
The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.
3) A Proximal Point method for Group and Fused Graphical Lasso
We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.
4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming
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`.
## 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.
[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.
1) ADMM for Single Graphical Lasso
2) ADMM for Group and Fused Graphical Lasso
The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.
3) A Proximal Point method for Group and Fused Graphical Lasso
We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.
4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming
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`.
## 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.
[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.
1) ADMM for Single Graphical Lasso
2) ADMM for Group and Fused Graphical Lasso
The algorithm was proposed in [2] and [3]. To use this, import `ADMM_MGL` from `gglasso/solver/admm_solver`.
3) A Proximal Point method for Group and Fused Graphical Lasso
We implement the PPDNA Algorithm like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.
4) ADMM method for Group Graphical Lasso where the features/variables are non-conforming
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`.
## 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 - 0.1.9-1
- Package Spec generated