GGLasso

This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent Graphical Lasso problems.

Docs | Examples

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 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 prior to submitting a pull request.
2. Report issues or problems with the software using github’s issue tracker.
3. Contributors must adhere to the Code of Conduct.

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.