pyhuge Python Package

pyhuge is the native Python package for high-dimensional undirected graph estimation and inference, sharing the same C++ core as the R huge package.

Table of contents

Background

pyhuge runs natively in Python with a shared C++ backend 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
  • Native extension: pyhuge._native_core is required for mb, tiger, glasso
  • Native source builds: Linux or macOS; Windows is not currently supported

Optional:

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

Installation

Published Linux x86_64 wheels bundle OpenBLAS and the OpenMP runtime:

pip install "pyhuge[runtime]"

Linux source builds require a C++17 compiler and OpenBLAS development files (libopenblas-dev on Ubuntu/Debian or openblas-devel on Fedora/RHEL).

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

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)

input_type="auto" preserves covariance detection by symmetry. For an observation matrix that is itself square and symmetric, set input_type="data" explicitly; use "covariance" to require covariance or correlation input.

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 2.0.0
bash scripts/release.sh 2.0.0

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}
}

Project links

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Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.0.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for tourzhao from gravatar.com tourzhao

Credits

Author: Tuo Zhao

GitHub Statistics

Data verified by PyPI on 
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

GPL-2.0-only
View SPDX License List

Requires

Python >=3.9

Provides Extra

runtime viz docs test dev release

Tags

graphical-model graphical-lasso sparse statistics native-python

Classifiers

Report project as malware

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