pyhuge 0.3.3

Native Python package for high-dimensional undirected graph estimation

pyhuge Python Package

pyhuge is a native Python package for high-dimensional undirected graph estimation and inference workflows inspired by huge.

Quick links

• PyPI: https://pypi.org/project/pyhuge/
• Documentation home: https://gatech-flash.github.io/huge/
• Getting started: https://gatech-flash.github.io/huge/getting-started/
• Installation: https://gatech-flash.github.io/huge/installation/
• API reference: https://gatech-flash.github.io/huge/api/
• Function manual index: https://gatech-flash.github.io/huge/man/index/
• Changelog: https://gatech-flash.github.io/huge/changelog/

Background

Compared with the earlier rpy2-bridge line, pyhuge runs natively in Python and does not require an R runtime.

Directory structure

• pyhuge/: package source code
• pyhuge/data/: packaged datasets (stockdata.npz)
• cpp/: optional pybind11 acceleration kernels
• tests/: unit/e2e/parity tests
• examples/: runnable scripts
• docs/: MkDocs documentation pages
• scripts/: release and docs helper scripts

What this package provides

• Core estimators: huge, huge_mb, huge_glasso, huge_ct, huge_tiger
• Model selection: huge_select (ric, stars, ebic)
• Data transforms and utilities: huge_npn, huge_generator, huge_roc, huge_inference
• Plotting helpers: huge_plot_sparsity, huge_plot_roc, huge_plot_graph_matrix, huge_plot_network, huge_plot
• Dataset helper: huge_stockdata
• Diagnostics: pyhuge.test(), pyhuge-doctor

Requirements

• Python >=3.9
• Runtime packages: numpy, scipy, scikit-learn

Optional:

• plotting: matplotlib, networkx
• docs: mkdocs, mkdocs-material

Installation

From source:

cd python-package
pip install -e ".[runtime]"

Optional extras:

pip install -e ".[viz]"
pip install -e ".[test]"
pip install -e ".[docs]"
pip install -e ".[dev]"

Runtime check:

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

If native C++ acceleration cannot be built on your machine, pyhuge falls back to pure Python automatically.

Usage

import numpy as np
from pyhuge import huge, huge_select

rng = np.random.default_rng(1)
x = rng.normal(size=(120, 30))

fit = huge(x, 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

• Docs source: python-package/docs
• Function manual pages: python-package/docs/man

Build locally:

cd python-package
mkdocs build --strict

Developer workflow

cd python-package
pytest
bash scripts/build_dist.sh
python scripts/bump_version.py <major.minor.patch>
bash scripts/release.sh <major.minor.patch>

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}
}