GGLasso

This package contains algorithms for solving Single and Multiple Graphical Lasso problems. Moreover, it contains the option of including latent variables.

Docs | Examples

Getting started

Clone the repository, for example with

git clone https://github.com/fabian-sp/GGLasso.git

Set up the dependencies with

pip install -r requirements.txt

In order to install gglasso in your Python environment, run

python setup.py

Test your installation with

pytest gglasso/ -v

Advanced options

If you want to install dependencies with conda, you can run

$ 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 you change the source code in your local repository, run

python setup.py clean --all develop clean --all

Algorithms

GGLasso contains several algorithms for Single and Multiple (i.e. Group and Fused) Graphical Lasso problems. Moreover, it allows to model latent variables (Latent variable Graphical Lasso) in order to estimate a precision matrix of for sparse - low rank.

1. 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.
   

2. A Proximal Point method for Group and Fused Graphical Lasso
   We implemented the PPDNA Algorithm implemented like proposed in [4]. To use this, import warmPPDNA from gglasso/solver/ppdna_solver.
   

3. ADMM for Single Graphical Lasso
   

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.
   

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.