This repo is the implementation of the review paper [1]
- Fused graphical lasso [2]
- Group graphical lasso [2]
- Graphical lasso [2]
- Doubly joint spike-and-slab graphical lasso [3]
- Anaconda Environment package:
conda env create -f environment.yml
conda activate r_env2 #activate environment
- Install R packages
Rscript install_packages.R
Saveral examples of data generation processes as well as sample codes are in the folder ./examples/jupyter_notebook
Sample code for data generation process 1 (DGP1). The instruction for running DGP2_roc.r is the same.
cd examples/roc
### Generate simulated data, the result will be stored in ./data
Rscript DGP1_roc.r DG [DATA DIMENSION]
### Select one of the refularization method FGL/GGL/GL. The result will be stored in ./results
Rscript DGP1_roc.r [ACTION: FGL/DGL/GL] [DATA DIMENSION]
###visualization
Rscript DGP1_roc_visualization.r
Please check the structure tree below for more details.
├── examples
│ ├── jupyter_notebook
| | ├── simple_example_block.ipynb
| | ├── simple_example_scalefree.ipynb
| | ├── simple_example_ssjgl.ipynb
│ │ └── simple_example.ipynb
│ │
│ ├── roc # run & visualize ROC curve
| | ├── DGP1_roc_visualization.r #visualization
│ | ├── DGP1_roc.r # roc curve on scalefree network, common structures share same inverse convarince matrix (data generation process 1)
| | |
| | ├── DGP2_roc_visualization.r #visualization
| | ├── DGP2_roc.r # roc curve on scalefree network, common structures have different inverse convarince matrices (data generation process 2)
| | |
| | ├── simple_roc_vis.r # visualization
| | └── simple_roc.r # roc curve on ramdom network
| |
| ├── joint_demo.r # beautiful result on random network (Erdos-Renyi graph)
│ ├── loss_graphsize_npAIC.r #fix p, vary n
│ ├── loss_smallgraphsize.r #fix n, vary n
│ ├── oos_scalefree.r # out-of-sample likelihood on scalefree network.
│ ├── oos.r # out-of-sample likelihood on random network
| ├── scalefree_AIC.r # model selection on scalefree network using AIC, tune the trucation value
| ├── scalefree_BIC.r # model selection on scalefree network using BIC, tune the trucation value
| ├── simple_example_ar.r # example on AR network: model selction, fnr,fpr, Frobenious loss, etropy loss
| └── simple_example_scalefree.r # example on scalefree network: model selction, fnr,fpr, Frobenious loss, etropy loss
|
├── R #source file
| ├── admm.iters.R
| ├── display.R
| ├── eval.R
| ├── gen_data.R
| ├── gete.R
| ├── JGL.R
| ├── metrics.R
| └── SSJGL.R
|
├── environment.yml
├── install_packages.R
├── README.md
└── .gitignore[1] Tsai, K., Koyejo, O., & Kolar, M. (2022). Joint Gaussian graphical model estimation: A survey. Wiley Interdisciplinary Reviews: Computational Statistics, e1582.
[2] Danaher, P., Wang, P., & Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society. Series B, Statistical methodology, 76(2), 373.
[3] Zehang Richard Li, Tyler H. McCormick, and Samuel J. Clark. "Bayesian joint spike-and-slab graphical lasso". International Conference on Machine Learning, 2019.