OPFython

OPFython is a Python implementation of the Optimum-Path Forest family of classifiers. It provides supervised, semi-supervised, unsupervised, and KNN-supervised models backed by NumPy and Numba.

This implementation follows LibOPF. Please cite the original LibOPF authors as well as OPFython when using it in research.

OPFython requires Python 3.11 or newer.

Installation

uv add opfython

Quick start

import numpy as np

from opfython.models import SupervisedOPF

X_train = np.asarray([[0.0, 0.0], [0.1, 0.2], [1.0, 1.0], [1.1, 0.9]])
Y_train = np.asarray([0, 0, 1, 1])
X_test = np.asarray([[0.05, 0.1], [1.05, 1.0]])

classifier = SupervisedOPF()
classifier.fit(X_train, Y_train)
predictions = classifier.predict(X_test)

Labels must be zero-based and sequential. Pre-computed distance matrices can be supplied through each classifier's pre_computed_distance constructor argument.

Classifiers

The package also includes 47 distance metrics, random generators, OPF evaluation measures, dataset loaders and splitters, package logging and exception helpers, and converters for LibOPF binary datasets.

See the documentation and the examples/applications directory for complete workflows.

Development

uv sync --all-groups
uv run pytest
uv run pre-commit run --all-files
uv run --group docs sphinx-build -W -b html docs docs/_build/html
uv build --no-sources

Citation

@article{rosa2021simpa,
    title = {OPFython: A Python implementation for Optimum-Path Forest},
    author = {Gustavo H. {de Rosa} and Joao P. Papa},
    journal = {Software Impacts},
    pages = {100113},
    year = {2021},
    issn = {2665-9638},
    doi = {https://doi.org/10.1016/j.simpa.2021.100113}
}

@misc{rosa2021speedup,
    title = {Speeding Up OPFython with Numba},
    author = {Gustavo H. de Rosa and Joao Paulo Papa},
    year = {2021},
    eprint = {2106.11828},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}

OPFython is licensed under the Apache License 2.0.