Hetnetpy is a Python 3 package for creating, querying, and operating on hetnets. This software provides convenient data structures for hetnets, as well as algorithms for edge prediction. It is specifically tailored and streamlined for hetnets compared to other more generic network software. See https://het.io/software for additional software packages designed specifically for hetnets.
Note that this package was previousely named hetio, available at the following repositories:
- https://github.com/hetio/hetnetpy (current)
- https://github.com/hetio/hetio (former)
- https://github.com/dhimmel/hetio (former)
In July 2019, the package was renamed to hetnetpy to more clearly represent its functionality and disambiguiate it from other products.
Hetnets: Hetnets, also called heterogeneous information networks, are graphs with multiple node and edge types. Hetnets are both multipartite and multirelational. They provide a scalable, intuitive, and frictionless structure for data integration.
Purpose: This package provides data structures for hetnets and algorithms for edge prediction. It only supports hetnets, which is its primary advantage compared to other network software. Node/edge attributes and edge directionality are supported.
Impetus: Development originated with a study to predict disease-associated genes and continues with a successive study to repurpose drugs.
Caution: Documentation is currently spotty, testing coverage is moderate, and the API is not fully stable. Contributions are welcome. Please use GitHub Issues for feedback, questions, or troubleshooting.
Please use Python 3.4 or higher. To install the current PyPI version (recommended), run:
pip install hetnetpyFor the latest GitHub version, run:
pip install git+https://github.com/hetio/hetnetpy.git#egg=hetnetpyFor development, clone or download-and-extract the repository. Then run pip install --editable . from the repository's root directory. The --editable flag specifies editable mode, so updating the source updates your installation.
Once installed, tests can be executed by running py.test test/ from the repository's root directory.
A Graph object stores a heterogeneous network and relies on the following classes:
- Graph
- MetaGraph
- Edge
- MetaEdge
This section is only relevant for project maintainers. Travis CI deployments are used to upload releases to PyPI and GitHub releases. To create a new release, do the following:
-
Bump the version in
__init__.py. -
Add a release notes file in
release-notes. Format as a commit message that will be used as the GitHub release description. -
Run the following commands:
TAG=v`python setup.py --version`
git add hetnetpy/__init__.py release-notes/$TAG.*
git commit --message "Prepare $TAG release"
git push
git tag --annotate $TAG --file release-notes/$TAG.*
git push upstream $TAG- Recommended: Edit the GitHub release to improve formatting and add a Zenodo badge.
This repository is dual licensed, available under either or both of the following licenses:
- BSD-2-Clause Plus Patent License at
LICENSE-BSD.md - CC0 1.0 Universal Public Domain Dedication at
LICENSE-CC0.md