anomalybench bundles anomaly detection algorithms, loaders for benchmark datasets and a command-line runner so that detectors can be compared on the same data under the same protocol. The base install covers classical detectors, ARIMA forecasting, graph detectors and the benchmark workflows; optional extras add deep-learning detectors (PyTorch and TensorFlow), streaming detectors (River) and Prophet-based forecasting. The bundled datasets span tabular, image, time-series and graph data.
Install
1
pip install anomalybench
The project targets Python 3.12 only for now; 3.13 is blocked until the runtime and dependency stack are validated there. Extras: deep, streaming, forecasting and all-detectors.
Project Links
- PyPI: anomalybench
- Documentation: diogoribeiro7.github.io/anomalybench
- Source: github.com/DiogoRibeiro7/anomalybench
- Issues: github.com/DiogoRibeiro7/anomalybench/issues
- Changelog: CHANGELOG.md
Package Metadata
- Current release:
0.6.1 - Requires Python:
>=3.12,<3.13 - Status: alpha
Where It Fits
Use it when the question is "which detector, on which kind of data, under which budget" rather than "how do I run one detector". It pairs with the anomaly-detection articles on this site, which lean on the same distinction between point, contextual and collective anomalies, and with cfad for the distribution-shape end of the problem.