https://github.com/sandialabs/conin

A python library that supports the constrained analysis of probabilistic graphical models
https://github.com/sandialabs/conin

scr-3226 snl-applications snl-data-analysis snl-science-libs

Last synced: 4 months ago
JSON representation

A python library that supports the constrained analysis of probabilistic graphical models

• Host: GitHub
• URL: https://github.com/sandialabs/conin
• Owner: sandialabs
• License: other
• Created: 2025-06-02T12:57:11.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2026-03-15T11:34:07.000Z (4 months ago)
• Last Synced: 2026-03-16T00:10:15.948Z (4 months ago)
• Topics: scr-3226, snl-applications, snl-data-analysis, snl-science-libs
• Language: Python
• Homepage:
• Size: 3.14 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md

Awesome Lists containing this project

README

          


A Python library that supports the constrained analysis of probabilistic graphical models 

--------------------------------------------------------------------------------

[![Pytest Tests](https://github.com/sandialabs/conin/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/sandialabs/conin/actions/workflows/pytest.yml?query=branch%3Amain)

[![codecov](https://codecov.io/gh/sandialabs/conin/branch/main/graph/badge.svg)](https://codecov.io/gh/sandialabs/conin)

[![Documentation Status](https://readthedocs.org/projects/conin/badge/?version=latest)](http://conin.readthedocs.org/en/latest/)

[![GitHub contributors](https://img.shields.io/github/contributors/sandialabs/conin.svg)](https://github.com/sandialabs/conin/graphs/contributors)

[![Merged PRs](https://img.shields.io/github/issues-pr-closed-raw/sandialabs/conin.svg?label=merged+PRs)](https://github.com/sandialabs/conin/pulls?q=is:pr+is:merged)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

## Overview

Conin supports constrained inference and learning for hidden Markov models, Bayesian networks, dynamic Bayesian networks and Markov networks. Conin interfaces with the pgmpy python library for the specification of general probabilistic graphical models. Additionally, it interfaces with a variety of optimization solvers to support learning and inference.

## Testing

Conin tests can be executed using pytest:

```

cd conin

pytest .

```

If the pytest-cov package is installed, pytest can provide coverage statistics:

```

cd conin

pytest --cov=conin .

```

The following options list the lines that are missing from coverage tests:

```

cd conin

pytest --cov=conin --cov-report term-missing .

```

Note that pytest coverage includes coverage of test files themselves.  This gives a somewhat skewed sense of coverage for the code base, but it helps identify tests that are omitted or not executed completely.