GGLasso - a Python package for General Graphical Lasso computation
GGLasso - a Python package for General Graphical Lasso computation - Published in JOSS (2021)
Science Score: 95.0%
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Published in Journal of Open Source Software
Keywords
Repository
A Python package for General Graphical Lasso computation
Basic Info
Statistics
- Stars: 36
- Watchers: 3
- Forks: 15
- Open Issues: 0
- Releases: 6
Topics
Metadata Files
README.md
GGLasso
This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent
Graphical Lasso problems.
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
Developer installation
If you want to create a conda environment with full development dependencies (for building docs, testing,...), run:
conda env create -f environment.yml
To install gglasso in developer mode run
python -m pip install --editable .
Test your installation with
pytest tests/ -v
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 solving a multitude of Graphical Lasso problem formulations. For all the details, we refer to the solver overview in our documentation.
The package includes solvers for the following problems:
Single Graphical Lasso
Group and Fused Graphical Lasso
We implemented the ADMM (see [2] and [3]) and a proximal point algorithm (see [4]).Non-conforming Group Graphical Lasso
A Group Graphical Lasso problem where not all variables exist in all instances/datasets.Functional Graphical Lasso
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 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.
Owner
- Login: fabian-sp
- Kind: user
- Location: Munich
- Company: TUM
- Repositories: 9
- Profile: https://github.com/fabian-sp
:bulb: Optimization :coffee: Machine Learning
JOSS Publication
GGLasso - a Python package for General Graphical Lasso computation
Authors
Technische Universität München
Institute of Computational Biology, Helmholtz Zentrum München, Department of Statistics, Ludwig-Maximilians-Universität München
Institute of Computational Biology, Helmholtz Zentrum München, Department of Statistics, Ludwig-Maximilians-Universität München, Center for Computational Mathematics, Flatiron Institute, New York
Editor
George K. Thiruvathukal
Tags
graphical lasso latent graphical model structured sparsity convex optimization ADMMPapers & Mentions
Total mentions: 3
Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models
- DOI: 10.1371/journal.pgen.1008766
- OpenAlex ID: https://openalex.org/W3021517978
- Published: May 2020
Variance Component Selection With Applications to Microbiome Taxonomic Data
- DOI: 10.3389/fmicb.2018.00509
- OpenAlex ID: https://openalex.org/W2794676611
- Published: March 2018
Genetic Diversity and Genome-Wide Association Study of Seed Aspect Ratio Using a High-Density SNP Array in Peanut (Arachis hypogaea L.)
- DOI: 10.3390/genes12010002
- OpenAlex ID: https://openalex.org/W3117497253
- Published: December 2020
GitHub Events
Total
- Release event: 1
- Watch event: 5
- Delete event: 2
- Push event: 7
- Fork event: 1
- Create event: 2
Last Year
- Release event: 1
- Watch event: 5
- Delete event: 2
- Push event: 7
- Fork event: 1
- Create event: 2
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| fabian-sp | f****p@h****e | 515 |
| Vlasovets | o****t@m****u | 161 |
| fabian-scalable | f****p@s****l | 55 |
| Christian L. Müller | m****n | 36 |
| Daniel S. Katz | d****z@i****g | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 7
- Total pull requests: 40
- Average time to close issues: 26 days
- Average time to close pull requests: 19 days
- Total issue authors: 6
- Total pull request authors: 3
- Average comments per issue: 4.29
- Average comments per pull request: 0.3
- Merged pull requests: 36
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: 3 months
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- papachristoumarios (2)
- myxa (1)
- Bpoole908 (1)
- ikarmann (1)
- DManowitz (1)
- lensory (1)
Pull Request Authors
- fabian-sp (30)
- Vlasovets (10)
- danielskatz (1)
Top Labels
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Packages
- Total packages: 2
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Total downloads:
- pypi 1,009 last-month
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Total dependent packages: 2
(may contain duplicates) -
Total dependent repositories: 2
(may contain duplicates) - Total versions: 13
- Total maintainers: 1
pypi.org: gglasso
Algorithms for Single and Multiple Graphical Lasso problems.
- Documentation: https://gglasso.readthedocs.io/en/stable/
- License: MIT License
-
Latest release: 0.2.1
published 10 months ago
Rankings
Maintainers (1)
conda-forge.org: gglasso
This package contains algorithms for solving General Graphical Lasso (GGLasso) problems, including single, multiple, as well as latent Graphical Lasso problems.
- Homepage: https://github.com/fabian-sp/GGLasso
- License: MIT
-
Latest release: 0.1.9
published over 3 years ago
Rankings
Dependencies
- decorator ==4.4.2
- matplotlib *
- networkx *
- numba >=0.46.0
- numpy >=1.17.3
- pandas *
- regain *
- scikit-learn >=0.24.1
- scipy >=0.11.0
- seaborn *
- sphinx ==3.5.4
- sphinx-gallery ==0.8.2
- sphinx_rtd_theme ==0.5.2