pyod and PyNomaly
PyOD is a comprehensive framework supporting multiple anomaly detection algorithms (classical and deep learning), while PyNomaly is a specialized implementation of a single local density-based method (LoOP), making them complementary tools where PyNomaly's approach could be one algorithm among many in a PyOD-based pipeline.
About pyod
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Provides 45+ detection algorithms unified under a scikit-learn compatible API, combining classical methods (LOF, Isolation Forest) with 12 PyTorch-based neural models. Emphasizes performance through Numba JIT compilation and the SUOD framework for fast training/prediction, plus LLM-guided automated model selection to reduce manual hyperparameter tuning.
About PyNomaly
vc1492a/PyNomaly
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
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