Metadata-Version: 2.1
Name: gglasso
Version: 0.1.2
Summary: Algorithms for Single and Multiple Graphical Lasso problems.
Home-page: https://github.com/fabian-sp/GGLasso
Author: Fabian Schaipp
Author-email: fabian.schaipp@tum.de
License: MIT
Description: 
        # GGLasso
        This package contains algorithms for solving Single and Multiple Graphical Lasso problems. Moreover, it contains the option of including latent variables.<br>
        
        [Docs](https://gglasso.readthedocs.io/en/latest/) | [Examples](https://gglasso.readthedocs.io/en/latest/auto_examples/index.html)
        
        ## 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*.
        <br>
        1) 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>
        
        2) A Proximal Point method for Group and Fused Graphical Lasso<br>
        We implemented the PPDNA Algorithm implemented like proposed in [4]. To use this, import `warmPPDNA` from `gglasso/solver/ppdna_solver`.<br>
        
        3) ADMM for Single Graphical Lasso<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>
        
        
        
        ## 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.
Keywords: network inference,graphcial models,graphical lasso,optimization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Operating System :: Unix
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
