https://github.com/abess-team/slide
[JASA] Reconstruct Ising Model with Global Optimality via SLIDE
https://github.com/abess-team/slide
binary-random-vector coupling ising-model pseudo-likelihood sparse-learning spin subset-selection
Last synced: 14 days ago
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[JASA] Reconstruct Ising Model with Global Optimality via SLIDE
- Host: GitHub
- URL: https://github.com/abess-team/slide
- Owner: abess-team
- Created: 2025-06-05T00:09:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-10-01T10:57:58.000Z (10 months ago)
- Last Synced: 2025-10-09T05:18:42.768Z (9 months ago)
- Topics: binary-random-vector, coupling, ising-model, pseudo-likelihood, sparse-learning, spin, subset-selection
- Language: R
- Homepage:
- Size: 809 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📘 Instructions for Reproducible Materials
> **Note:** The implementation of **SLIDE** is provided by the R function `slide` in the **`abess`** package on CRAN.
---
## 🗂️ Project Organization
- 🖥️ **Bash script** (`batch.sh`): Automates execution of all numerical simulation studies.
- 📊 **R scripts** (`.R`): Implement baseline methods, evaluation metrics, simulation studies, and real-data analysis of the congressional voting dataset.
- 📁 **Data files** (`.csv`): Used for real-world data analysis. Available on
👉 [Dropbox](https://www.dropbox.com/scl/fo/zbfrhxm60y8hhrzufhno2/AJzjVAZiJHK8AhrrBS6xxUw?rlkey=fpjf3h5awrki1cik5ypy5pqg9&st=8qwgpufi&dl=0).
---
## 📄 File Descriptions
### 🔧 Main R Scripts
- `simu_degree.R` — empirical sample complexity analysis w.r.t. *degree*.
- `simu_beta.R` — empirical sample complexity analysis w.r.t. *maximum signal*.
- `simu_high.R` — experiments for *high-dimensional* cases.
- `simu_p.R` — empirical sample complexity analysis w.r.t. *dimension*.
- `simu_ws.R` — empirical sample complexity analysis w.r.t. *weakest signal*.
- `DataAnalysis.R` — real-data analysis, including data cleaning, estimation of graphical structure among senators, and visualization.
### 🧩 Utility R Scripts *(automatically used by main scripts)*
- `simulation_main.R` — executes a method on a simulated dataset.
- `method_implementation.R` — implementations of baseline methods (RPLE, RISE, logRISE, ELASSO, RLRF).
- `evaluation.R` — evaluation metrics (e.g., Frobenius norm, true positive rate).
---
## 🔁 Reproducing Results
Each script corresponds to figures/tables in the paper:
| Script | Output Figures / Tables |
|--------|--------------------------|
| `simu_degree.R` | **Figure 1**, **Table S1** |
| `simu_beta.R` | **Figure 2**, **Figure S1** |
| `simu_high.R` | **Figure 3**, **Figure S2** |
| `simu_p.R` | **Figure S3** |
| `simu_ws.R` | **Figure S4** |
| `DataAnalysis.R` | **Figure 4** |
---
## ▶️ Quick Start: Reproduce All Results
1. 📈 **Reproduce real-data results** (Figure 4):
```r
Rscript DataAnalysis.R
```
2. 🚀 **Run simulations** using the bash script:
```bash
bash batch.sh
```
---