pyhuge 0.2.1

Python wrapper for the R package huge

pyhuge Python Package

pyhuge is the Python wrapper for the huge package: High-dimensional Undirected Graph Estimation.

Table of contents

• Background
• Directory structure
• What this package provides
• Requirements
• Installation
• Usage
• Documentation and tutorials
• Performance notes
• Implemented APIs
• For developer
• Citation
• References

Background

huge is widely used for sparse undirected graphical model estimation in high dimensions. pyhuge keeps the original R/C++ backend and exposes a Python API via rpy2 so Python users can:

• call the same core methods (mb, glasso, ct, tiger);
• run model selection (ric, stars, ebic);
• access simulation, ROC, and inference helpers;
• work with numpy and scipy.sparse objects directly.

Directory structure

The Python package directory is organized as:

• pyhuge/: Python wrapper source code
• tests/: unit/runtime/e2e tests
• examples/: runnable scripts
• docs/: MkDocs documentation pages
• scripts/: release and docs build scripts

What this package provides

• Thin and stable bridge to mature huge backend.
• Argument validation with explicit PyHugeError messages.
• Typed result objects.
• Plot helpers including node-edge network visualization (huge_plot_network).
• Tests, documentation website workflow, and release automation scripts.

Requirements

• Python >=3.9
• R installed and available in PATH
• R package huge installed (with Rcpp, RcppEigen, igraph, etc.)
• Python and R architecture must match (both arm64 or both x86_64)

Installation

Install from source:

git clone https://github.com/Gatech-Flash/huge.git
cd huge/python-package
pip install -e .

Install directly from GitHub:

pip install "git+https://github.com/Gatech-Flash/huge.git#subdirectory=python-package"

Optional extras:

pip install -e ".[viz]"      # matplotlib + networkx
pip install -e ".[release]"  # build + twine
pip install -e ".[dev]"      # test + docs + release deps

Runtime check:

python -c "import pyhuge; print(pyhuge.test())"

If runtime=False, verify R_LIBS_USER, architecture match, and huge visibility in R.

Usage

import numpy as np
from pyhuge import huge, huge_npn, huge_select

rng = np.random.default_rng(1)
x = rng.normal(size=(120, 30))
x_npn = huge_npn(x, npn_func="shrinkage", verbose=False)

fit = huge(x_npn, method="mb", nlambda=8, verbose=False)
sel = huge_select(fit, criterion="ric", verbose=False)
print(fit.method, len(fit.path), sel.opt_lambda, sel.opt_sparsity)

Network visualization:

import matplotlib.pyplot as plt
from pyhuge import huge_plot_network

fig, ax = plt.subplots(figsize=(5, 5))
huge_plot_network(fit, index=-1, ax=ax, layout="spring")
plt.show()

Documentation and tutorials

• Website: https://gatech-flash.github.io/huge/
• Docs source: python-package/docs
• Function manual directory: python-package/docs/man (R man/-style layout)
• Tutorial scripts: python-package/examples

Build docs locally:

cd python-package
mkdocs serve

Run tutorials/examples:

cd python-package
python examples/run_huge_mb.py
python examples/run_summary_and_plot.py
python examples/run_network_plot.py

If you use custom R library path:

R_LIBS_USER=/Users/tourzhao/Desktop/huge-master/.Rlib \
python examples/run_huge_mb.py

Performance notes

pyhuge runtime is dominated by R backend solve time for medium/large problems. For many tiny repeated calls, Python-R bridge overhead can matter. Benchmark backend and end-to-end workflows separately when tuning performance.

Implemented APIs

• huge, huge_mb, huge_glasso, huge_ct, huge_tiger
• huge_select, huge_npn
• huge_generator, huge_inference, huge_roc
• huge_summary, huge_select_summary
• huge_plot_sparsity, huge_plot_roc, huge_plot_graph_matrix, huge_plot_network
• test

For developer

cd python-package
pip install -e ".[dev]"
pytest
mkdocs build --strict
bash scripts/build_docs.sh
bash scripts/build_dist.sh

Optional e2e run with local R runtime:

export R_LIBS_USER=/Users/tourzhao/Desktop/huge-master/.Rlib
export PYHUGE_REQUIRE_RUNTIME=1
pytest tests/test_e2e_optional.py -rA

Workflows:

• .github/workflows/python-wrapper-tests.yml
• .github/workflows/python-package-docs.yml
• .github/workflows/python-package-release.yml

Citation

If you use huge / pyhuge in research, cite:

@article{zhao2012huge,
  title   = {The huge Package for High-dimensional Undirected Graph Estimation in R},
  author  = {Zhao, Tuo and Liu, Han and Roeder, Kathryn and Lafferty, John and Wasserman, Larry},
  journal = {Journal of Machine Learning Research},
  volume  = {13},
  pages   = {1059--1062},
  year    = {2012}
}

References

• T. Zhao, H. Liu, K. Roeder, J. Lafferty, and L. Wasserman, The huge Package for High-dimensional Undirected Graph Estimation in R, JMLR 13:1059-1062, 2012.
• Huge CRAN page: https://cran.r-project.org/package=huge
• Repository: https://github.com/Gatech-Flash/huge