Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'

TL;DR

This paper introduces 'nonconform', a Python package that applies conformal anomaly detection to provide statistically calibrated and interpretable anomaly scores, improving upon heuristic thresholds.

Contribution

It presents a practical implementation of conformal anomaly detection in Python, integrating with popular ML tools and supporting various calibration strategies.

Findings

Empirical results show statistically principled anomaly detection.

The package supports multiple conformalization strategies.

It enhances reproducibility and interpretability in anomaly detection workflows.

Abstract

Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation. Conformal anomaly detection addresses this limitation by converting anomaly scores into calibrated p-values that are valid under the statistical assumption of data exchangeability, with a growing literature extending this idea beyond that setting. We present 'nonconform', a Python package for applying conformal anomaly detection within existing machine-learning workflows, and use it as the basis for an implementation-grounded introduction to the field. The package integrates with 'scikit-learn', 'pyod', and custom anomaly detectors, and provides a unified interface for calibration, p-value generation, and false discovery rate control. It supports several conformalization strategies, ranging from simple split-conformal calibration to more data-efficient and shift-aware extensions. Through a progression from foundational concepts to advanced conformalization strategies, complemented by code examples, the paper connects the statistical ideas behind conformal anomaly detection to their practical use in 'nonconform'. Empirical results demonstrate that the implemented methods enable statistically principled anomaly detection. Together, the package and exposition aim to make core conformal anomaly detection workflows more accessible and reproducible in experimental and production-oriented settings.