Calibrate scores. Control discoveries. Monitor change.

Documentation · Batch workflow · Sequential workflow · API reference · Paper

nonconform turns anomaly scores into conformal evidence for two primary workflows: batch discovery control and sequential change monitoring. Wrap a supported scikit-learn estimator, a PyOD model, or a custom detector:

• Batch: Use calibrated p-values directly or call select(...) to apply false discovery rate (FDR) control.
• Stream: Use conformal martingales to accumulate evidence against exchangeability and trigger configured alarms.

Why nonconform?

• Calibrate anomaly scores into conformal p-values using reference data.
• Control batch discoveries by accounting for multiple tests within a fixed family.
• Monitor streams for change with conformal martingales, anytime evidence against exchangeability, and configurable alarms.
• Keep your detector through support for PyOD, recognized scikit-learn estimators, and protocol-compliant custom models.

Works with              

Installation

nonconform requires Python 3.12 or newer. Both batch discovery control and sequential monitoring are included in the core installation.

pip install nonconform

For the PyOD detector collection and benchmark datasets:

pip install "nonconform[pyod,data]"

Optional extras

Extra	Adds
pyod	PyOD anomaly detectors
data	oddball benchmark datasets and PyArrow support
fdr	Streaming FDR procedures from online-fdr
probabilistic	KDE-based probabilistic estimation and tuning
all	Every optional feature

Quick start

Batch discovery control

This core-only example demonstrates the batch lane. The detector is trained on normal data, part of which is reserved automatically for conformal calibration.

import numpy as np
from sklearn.ensemble import IsolationForest

from nonconform import ConformalDetector, Split

rng = np.random.default_rng(42)
x_train = rng.normal(size=(1_000, 2))
x_test = np.vstack([
    rng.normal(size=(200, 2)),
    rng.normal(loc=5.0, size=(20, 2)),
])

detector = ConformalDetector(
    detector=IsolationForest(random_state=42),
    strategy=Split(n_calib=0.3),
    seed=42,
).fit(x_train)

discoveries = detector.select(x_test, alpha=0.05)
p_values = detector.last_result.p_values

print(f"Selected {discoveries.sum()} of {len(x_test)} observations")

Note

discoveries is a Boolean mask. Here, alpha=0.05 is the target FDR level, not a per-observation score threshold. The underlying conformal p-values remain available through last_result for inspection or downstream analysis.

Sequential change monitoring

A fitted Split detector can initialize the stream lane without refitting its scoring model. The example is self-contained so it can be copied independently of the batch example.

Show sequential monitoring example

import numpy as np
from sklearn.ensemble import IsolationForest

from nonconform import ConformalDetector, Split
from nonconform.martingales import AlarmConfig, SimpleJumperMartingale
from nonconform.monitoring import ExchangeabilityMonitor

rng = np.random.default_rng(42)
x_train = rng.normal(size=(1_000, 2))

detector = ConformalDetector(
    detector=IsolationForest(random_state=42),
    strategy=Split(n_calib=0.3),
    seed=42,
).fit(x_train)

alpha = 0.05
monitor = ExchangeabilityMonitor.from_split_detector(
    detector,
    martingale=SimpleJumperMartingale(
        alarm_config=AlarmConfig(restarted_ville_threshold=1 / alpha)
    ),
    seed=42,
)

# Stable observations followed by a distribution shift
x_stream = np.vstack([
    rng.normal(size=(50, 2)),
    rng.normal(loc=3.0, size=(50, 2)),
])

for x_t in x_stream:
    state = monitor.update(x_t)
    if "restarted_ville" in state.triggered_alarms:
        print(f"Change alarm at step {state.evidence_step}")
        break

Under the sequential validity assumptions, the restarted Ville alarm at 1 / alpha controls the probability of ever crossing on one stream. It does not control FDR across multiple streams. See the sequential monitoring guide for the full guarantee scope and other alarm statistics.

Choose a workflow

Goal	Start with
Calibrate and select anomalies in a batch	Split and select(...)
Aggregate evidence across random splits	DerandomizedSplits and e-BH
Monitor a stream for change	Exchangeability martingales
Reuse more data for fitting and calibration	CrossValidation or JackknifeBootstrap
Account for covariate shift	Weighted conformal inference
Certify realized FDP across p-value thresholds	fdp_bounds(...) and FDP certificates
Bring a custom or third-party detector	Detector compatibility

Statistical scope

Important

Guarantees are assumption-dependent. Standard conformal workflows require calibration data and null test cases to be exchangeable. FDR claims additionally require valid p-values or the applicable aggregate null-evidence condition, together with the assumptions of the selected multiple-testing procedure. Weighted workflows require a plausible covariate-shift model, support overlap, and reliable weights. Sequential martingales require valid sequential conformal p-values; Ville thresholds provide false-alarm control for one valid stream, while CUSUM and Shiryaev-Roberts thresholds are change-evidence triggers that require separate calibration.

nonconform calibrates detector scores; it cannot make an unsuitable detector or mismatched calibration set valid. Spatial or temporal dependence must be handled explicitly before applying standard exchangeability-based claims. See the guides to FDR control and sequential monitoring before relying on error-control statements in a new application.

Citation

If you use nonconform in academic work, please cite the accompanying paper:

@inproceedings{Hennhofer_Conformal_Anomaly_Detection_2026,
author = {Hennhöfer, Oliver and Kirsch, Maximilian and Preisach, Christine},
booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications},
pages = {613--632},
publisher = {PMLR},
title = {{Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with nonconform}},
url = {https://proceedings.mlr.press/v329/hennhofer26a.html},
year = {2026}
}

Project

Read the documentation, browse the changelog, or report a problem in the issue tracker. Contributions are welcome; start with the contributing guide. nonconform is distributed under the BSD 3-Clause License.

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