path: root/python-gglasso.spec

%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

[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
[![Python version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)
[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](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

[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
[![Python version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)
[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](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

[![PyPI version fury.io](https://badge.fury.io/py/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
[![PyPI license](https://img.shields.io/pypi/l/gglasso.svg)](https://pypi.python.org/pypi/gglasso/)
[![Python version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
[![Documentation Status](https://readthedocs.org/projects/gglasso/badge/?version=latest)](http://gglasso.readthedocs.io/?badge=latest)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.03865/status.svg)](https://doi.org/10.21105/joss.03865)
[![arXiv](https://img.shields.io/badge/arXiv-2011.00898-b31b1b.svg)](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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.9-1
- Package Spec generated