# GGLasso
[](https://pypi.python.org/pypi/gglasso/)
[](https://pypi.python.org/pypi/gglasso/)
[](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/conda
The package is available on pip and conda and can be installed with
pip install gglasso
or
conda install -c conda-forge 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
If you want to create a conda environment with full development dependencies (for building docs, testing etc), run:
conda env create -f environment.yml
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 solving a multitude of Graphical Lasso problem formulations. For all the details, we refer to the [solver overview in our documentation](https://gglasso.readthedocs.io/en/latest/solvers-overview.html).
The package includes solvers for the following problems:<br>
- **Single Graphical Lasso**<br>
- **Group and Fused Graphical Lasso**<br>
We implemented the ADMM (see [2] and [3]) and a proximal point algorithm (see [4]).
- **Non-conforming Group Graphical Lasso**<br>
A Group Graphical Lasso problem where not all variables exist in all instances/datasets.
- **Functional Graphical Lasso**<br>
A variant of Graphical Lasso where each variables has a functional representation (e.g. by Fourier coefficients).
Moreover, for all problem formulation the package allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type *sparse - low rank*.
## 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.
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"description": "\n# GGLasso\n\n[](https://pypi.python.org/pypi/gglasso/)\n[](https://pypi.python.org/pypi/gglasso/)\n[](http://gglasso.readthedocs.io/?badge=latest)\n[](https://doi.org/10.21105/joss.03865)\n[](https://arxiv.org/abs/2110.10521)\n\n\nThis package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent \nGraphical Lasso problems. <br>\n\n[Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html)\n\n## Getting started\n\n### Install via pip/conda\n\nThe package is available on pip and conda and can be installed with\n\n pip install gglasso\n\nor\n\n conda install -c conda-forge gglasso\n\n\n### Install from source\n\nAlternatively, you can install the package from source using the following commands:\n\n git clone https://github.com/fabian-sp/GGLasso.git\n pip install -r requirements.txt\n python setup.py\n\nTest your installation with \n\n pytest gglasso/ -v\n\n\n### Advanced options\n\nIf you want to create a conda environment with full development dependencies (for building docs, testing etc), run:\n\n\tconda env create -f environment.yml\n\nIf 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\n\n python setup.py clean --all develop clean --all\n\n## The `glasso_problem` class\n\n`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.\n\n\n## Algorithms\n\n`GGLasso` contains algorithms for solving a multitude of Graphical Lasso problem formulations. For all the details, we refer to the [solver overview in our documentation](https://gglasso.readthedocs.io/en/latest/solvers-overview.html).\n\nThe package includes solvers for the following problems:<br>\n\n- **Single Graphical Lasso**<br>\n\n- **Group and Fused Graphical Lasso**<br>\nWe implemented the ADMM (see [2] and [3]) and a proximal point algorithm (see [4]). \n\n- **Non-conforming Group Graphical Lasso**<br>\nA Group Graphical Lasso problem where not all variables exist in all instances/datasets. \n\n- **Functional Graphical Lasso**<br>\nA variant of Graphical Lasso where each variables has a functional representation (e.g. by Fourier coefficients).\n\nMoreover, for all problem formulation the package allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of type *sparse - low rank*.\n\n## Citation\n\nIf you use `GGLasso`, please consider the following citation\n\n @article{Schaipp2021,\n doi = {10.21105/joss.03865},\n url = {https://doi.org/10.21105/joss.03865},\n year = {2021},\n publisher = {The Open Journal},\n volume = {6},\n number = {68},\n pages = {3865},\n author = {Fabian Schaipp and Oleg Vlasovets and Christian L. M\u00fcller},\n title = {GGLasso - a Python package for General Graphical Lasso computation},\n journal = {Journal of Open Source Software}\n }\n\n\n## Community Guidelines\n\n1) Contributions and suggestions to the software are always welcome.\n Please, consult our [contribution guidelines](CONTRIBUTING.md) prior\n to submitting a pull request.\n2) Report issues or problems with the software using github\u2019s [issue\n tracker](https://github.com/fabian-sp/GGLasso/issues).\n3) Contributors must adhere to the [Code of\n Conduct](CODE_OF_CONDUCT.md).\n\n\n## References\n* [1] Friedman, J., Hastie, T., and Tibshirani, R. (2007). Sparse inverse covariance estimation with the Graphical Lasso. Biostatistics, 9(3):432\u2013441.\n* [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\u2013397.\n* [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.\n* [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\u20132220.\n\n\n",
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