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nonconform

Turn anomaly scores into statistically valid decisions.

Traditional anomaly detectors output scores and require arbitrary thresholds. nonconform converts raw scores to conformal p-values and supports principled False Discovery Rate (FDR) control for final decisions.

The Problem

# Traditional approach: arbitrary threshold, no formal error control
scores = detector.decision_function(X_test)
anomalies = scores > 0.5

The Solution

from sklearn.ensemble import IsolationForest

from nonconform import ConformalDetector, Split

detector = ConformalDetector(
    detector=IsolationForest(random_state=42),
    strategy=Split(),
    score_polarity="auto",
)
detector.fit(X_train)

discoveries = detector.select(X_test, alpha=0.05)

When to Use nonconform

Use this library when you need:

  • Statistical guarantees on anomaly decisions
  • Principled thresholds instead of ad hoc cutoffs
  • Multiple testing correction
  • Calibrated uncertainty for downstream workflows

Key Features

  • Conformal inference with finite-sample guarantees
  • Detector agnostic design (PyOD, scikit-learn, custom detectors)
  • Multiple calibration strategies (Split, CV, Jackknife+ variants)
  • FDR and weighted FDR workflows
  • Covariate-shift handling via weighted conformal methods

Installation

pip install nonconform

uv add nonconform

Ready to start? Continue with the Quick Start guide.