| Name | nonconform JSON |
| Version |
0.91.0
JSON |
| download |
| home_page | None |
| Summary | Conformal Anomaly Detection |
| upload_time | 2025-10-09 12:51:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | BSD 3-Clause License
Copyright (c) 2024, Oliver Hennhöfer
Redistribution and use in source and binary forms, with or without
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| keywords |
anomaly detection
conformal anomaly detection
conformal inference
false discovery rate
uncertainty quantification
|
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---
[](https://github.com/psf/black)
[](https://github.com/astral-sh/ruff)
**nonconform** enhances anomaly detection by providing uncertainty quantification. It acts as a wrapper around most detectors from [*PyOD*](https://pyod.readthedocs.io/en/latest/) (see [Supported Estimators](#supported-estimators)). By leveraging one-class classification and **conformal inference**, **nonconform** enables **statistically rigorous anomaly detection**.
* **Uncertainty Quantification:** Turn anomaly scores into statistically valid _p_-values.
* **Control False Positives:** Reliably control metrics like the False Discovery Rate (FDR).
* **[*PyOD*](https://pyod.readthedocs.io/en/latest/) Compatibility:** Works with most PyOD anomaly detectors (see [Supported Estimators](#supported-estimators)).
# Getting Started
Installation via [PyPI](https://pypi.org/project/nonconform/):
```sh
pip install nonconform
```
> **Note:** The following examples use the built-in datasets. Install with `pip install nonconform[data]` to run these examples. (see [Optional Dependencies](#optional-dependencies))
## Classical (Conformal) Approach
**Example:** Detecting anomalies with Isolation Forest on the Shuttle dataset. The approach splits data for calibration, trains the model, then converts anomaly scores to statistically valid p-values by comparing test scores against the calibration distribution.
```python
from pyod.models.iforest import IForest
from scipy.stats import false_discovery_control
from nonconform.strategy import Split
from nonconform.estimation import ConformalDetector
from nonconform.utils.data import load, Dataset
from nonconform.utils.stat import false_discovery_rate, statistical_power
x_train, x_test, y_test = load(Dataset.SHUTTLE, setup=True, seed=42)
estimator = ConformalDetector(
detector=IForest(behaviour="new"),
strategy=Split(n_calib=2_000),
seed=42
)
estimator.fit(x_train)
estimates = estimator.predict(x_test)
decisions = false_discovery_control(estimates, method='bh') <= 0.2
print(f"Empirical False Discovery Rate: {false_discovery_rate(y=y_test, y_hat=decisions)}")
print(f"Empirical Statistical Power (Recall): {statistical_power(y=y_test, y_hat=decisions)}")
```
Output:
```text
Empirical False Discovery Rate: 0.198
Empirical Statistical Power (Recall): 0.97
```
# Advanced Methods
For advanced use cases, the unified ``ConformalDetector()`` supports weighted conformal prediction (robust to covariate shifts) by adding a weight_estimator parameter, and sophisticated calibration strategies like ``JackknifeBootstrap()`` for improved results.
# Beyond Static Data
While primarily designed for static (single-batch) applications, the library supports streaming scenarios through ``BatchGenerator()`` and ``OnlineGenerator()``. For statistically valid FDR control in streaming data, use the optional ``onlineFDR`` dependency, which implements appropriate statistical methods.
# Citation
If you find this repository useful for your research, please cite the following papers:
##### Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors
```text
@inproceedings{Hennhofer2024,
title = {{ Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors }},
author = {Hennhofer, Oliver and Preisach, Christine},
year = 2024,
month = {Dec},
booktitle = {2024 IEEE International Conference on Knowledge Graph (ICKG)},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
pages = {110--119},
doi = {10.1109/ICKG63256.2024.00022},
url = {https://doi.ieeecomputersociety.org/10.1109/ICKG63256.2024.00022}
}
```
##### Testing for Outliers with Conformal p-Values
```text
@article{Bates2023,
title = {Testing for outliers with conformal p-values},
author = {Bates, Stephen and Candès, Emmanuel and Lei, Lihua and Romano, Yaniv and Sesia, Matteo},
year = 2023,
month = feb,
journal = {The Annals of Statistics},
publisher = {Institute of Mathematical Statistics},
volume = 51,
number = 1,
doi = {10.1214/22-aos2244},
issn = {0090-5364},
url = {http://dx.doi.org/10.1214/22-AOS2244}
}
```
# Optional Dependencies
_For additional features, you might need optional dependencies:_
- `pip install nonconform[data]` - Includes pyarrow for loading example data (via remote download)
- `pip install nonconform[deep]` - Includes deep learning dependencies (PyTorch)
- `pip install nonconform[fdr]` - Includes advanced FDR control methods (online-fdr)
- `pip install nonconform[dev]` - Includes development documentation tools
- `pip install nonconform[all]` - Includes all optional dependencies
_Please refer to the [pyproject.toml](https://github.com/OliverHennhoefer/nonconform/blob/main/pyproject.toml) for details._
# Supported Estimators
Only anomaly estimators suitable for unsupervised one-class classification are supported. Since detectors are trained exclusively on normal data, threshold parameters are automatically set to minimal values.
Models that are **currently supported** include:
* Angle-Based Outlier Detection (**ABOD**)
* Autoencoder (**AE**)
* Cook's Distance (**CD**)
* Copula-based Outlier Detector (**COPOD**)
* Deep Isolation Forest (**DIF**)
* Empirical-Cumulative-distribution-based Outlier Detection (**ECOD**)
* Gaussian Mixture Model (**GMM**)
* Histogram-based Outlier Detection (**HBOS**)
* Isolation-based Anomaly Detection using Nearest-Neighbor Ensembles (**INNE**)
* Isolation Forest (**IForest**)
* Kernel Density Estimation (**KDE**)
* *k*-Nearest Neighbor (***k*NN**)
* Kernel Principal Component Analysis (**KPCA**)
* Linear Model Deviation-base Outlier Detection (**LMDD**)
* Local Outlier Factor (**LOF**)
* Local Correlation Integral (**LOCI**)
* Lightweight Online Detector of Anomalies (**LODA**)
* Locally Selective Combination of Parallel Outlier Ensembles (**LSCP**)
* GNN-based Anomaly Detection Method (**LUNAR**)
* Median Absolute Deviation (**MAD**)
* Minimum Covariance Determinant (**MCD**)
* One-Class SVM (**OCSVM**)
* Principal Component Analysis (**PCA**)
* Quasi-Monte Carlo Discrepancy Outlier Detection (**QMCD**)
* Rotation-based Outlier Detection (**ROD**)
* Subspace Outlier Detection (**SOD**)
* Scalable Unsupervised Outlier Detection (**SUOD**)
# Contact
**Bug reporting:** [https://github.com/OliverHennhoefer/nonconform/issues](https://github.com/OliverHennhoefer/nonconform/issues)
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"description": "\n\n---\n\n\n\n\n\n[](https://github.com/psf/black)\n[](https://github.com/astral-sh/ruff)\n\n\n\n\n\n**nonconform** enhances anomaly detection by providing uncertainty quantification. It acts as a wrapper around most detectors from [*PyOD*](https://pyod.readthedocs.io/en/latest/) (see [Supported Estimators](#supported-estimators)). By leveraging one-class classification and **conformal inference**, **nonconform** enables **statistically rigorous anomaly detection**.\n\n* **Uncertainty Quantification:** Turn anomaly scores into statistically valid _p_-values.\n* **Control False Positives:** Reliably control metrics like the False Discovery Rate (FDR).\n* **[*PyOD*](https://pyod.readthedocs.io/en/latest/) Compatibility:** Works with most PyOD anomaly detectors (see [Supported Estimators](#supported-estimators)).\n\n# Getting Started\n\nInstallation via [PyPI](https://pypi.org/project/nonconform/):\n```sh\npip install nonconform\n```\n\n> **Note:** The following examples use the built-in datasets. Install with `pip install nonconform[data]` to run these examples. (see [Optional Dependencies](#optional-dependencies))\n\n\n## Classical (Conformal) Approach\n\n**Example:** Detecting anomalies with Isolation Forest on the Shuttle dataset. The approach splits data for calibration, trains the model, then converts anomaly scores to statistically valid p-values by comparing test scores against the calibration distribution.\n\n```python\nfrom pyod.models.iforest import IForest\nfrom scipy.stats import false_discovery_control\n\nfrom nonconform.strategy import Split\nfrom nonconform.estimation import ConformalDetector\nfrom nonconform.utils.data import load, Dataset\nfrom nonconform.utils.stat import false_discovery_rate, statistical_power\n\nx_train, x_test, y_test = load(Dataset.SHUTTLE, setup=True, seed=42)\n\nestimator = ConformalDetector(\n detector=IForest(behaviour=\"new\"),\n strategy=Split(n_calib=2_000),\n seed=42\n)\n\nestimator.fit(x_train)\n\nestimates = estimator.predict(x_test)\ndecisions = false_discovery_control(estimates, method='bh') <= 0.2\n\nprint(f\"Empirical False Discovery Rate: {false_discovery_rate(y=y_test, y_hat=decisions)}\")\nprint(f\"Empirical Statistical Power (Recall): {statistical_power(y=y_test, y_hat=decisions)}\")\n```\n\nOutput:\n```text\nEmpirical False Discovery Rate: 0.198\nEmpirical Statistical Power (Recall): 0.97\n```\n\n# Advanced Methods\n\nFor advanced use cases, the unified ``ConformalDetector()`` supports weighted conformal prediction (robust to covariate shifts) by adding a weight_estimator parameter, and sophisticated calibration strategies like ``JackknifeBootstrap()`` for improved results.\n\n\n# Beyond Static Data\n\nWhile primarily designed for static (single-batch) applications, the library supports streaming scenarios through ``BatchGenerator()`` and ``OnlineGenerator()``. For statistically valid FDR control in streaming data, use the optional ``onlineFDR`` dependency, which implements appropriate statistical methods.\n\n\n# Citation\n\nIf you find this repository useful for your research, please cite the following papers:\n\n##### Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors\n```text\n@inproceedings{Hennhofer2024,\n\ttitle = {{ Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors }},\n\tauthor = {Hennhofer, Oliver and Preisach, Christine},\n\tyear = 2024,\n\tmonth = {Dec},\n\tbooktitle = {2024 IEEE International Conference on Knowledge Graph (ICKG)},\n\tpublisher = {IEEE Computer Society},\n\taddress = {Los Alamitos, CA, USA},\n\tpages = {110--119},\n\tdoi = {10.1109/ICKG63256.2024.00022},\n\turl = {https://doi.ieeecomputersociety.org/10.1109/ICKG63256.2024.00022}\n}\n```\n\n##### Testing for Outliers with Conformal p-Values\n```text\n@article{Bates2023,\n\ttitle = {Testing for outliers with conformal p-values},\n\tauthor = {Bates, Stephen and Cand\u00e8s, Emmanuel and Lei, Lihua and Romano, Yaniv and Sesia, Matteo},\n\tyear = 2023,\n\tmonth = feb,\n\tjournal = {The Annals of Statistics},\n\tpublisher = {Institute of Mathematical Statistics},\n\tvolume = 51,\n\tnumber = 1,\n\tdoi = {10.1214/22-aos2244},\n\tissn = {0090-5364},\n\turl = {http://dx.doi.org/10.1214/22-AOS2244}\n}\n```\n\n# Optional Dependencies\n\n_For additional features, you might need optional dependencies:_\n- `pip install nonconform[data]` - Includes pyarrow for loading example data (via remote download)\n- `pip install nonconform[deep]` - Includes deep learning dependencies (PyTorch)\n- `pip install nonconform[fdr]` - Includes advanced FDR control methods (online-fdr)\n- `pip install nonconform[dev]` - Includes development documentation tools\n- `pip install nonconform[all]` - Includes all optional dependencies\n\n_Please refer to the [pyproject.toml](https://github.com/OliverHennhoefer/nonconform/blob/main/pyproject.toml) for details._\n\n# Supported Estimators\n\nOnly anomaly estimators suitable for unsupervised one-class classification are supported. Since detectors are trained exclusively on normal data, threshold parameters are automatically set to minimal values.\n\nModels that are **currently supported** include:\n\n* Angle-Based Outlier Detection (**ABOD**)\n* Autoencoder (**AE**)\n* Cook's Distance (**CD**)\n* Copula-based Outlier Detector (**COPOD**)\n* Deep Isolation Forest (**DIF**)\n* Empirical-Cumulative-distribution-based Outlier Detection (**ECOD**)\n* Gaussian Mixture Model (**GMM**)\n* Histogram-based Outlier Detection (**HBOS**)\n* Isolation-based Anomaly Detection using Nearest-Neighbor Ensembles (**INNE**)\n* Isolation Forest (**IForest**)\n* Kernel Density Estimation (**KDE**)\n* *k*-Nearest Neighbor (***k*NN**)\n* Kernel Principal Component Analysis (**KPCA**)\n* Linear Model Deviation-base Outlier Detection (**LMDD**)\n* Local Outlier Factor (**LOF**)\n* Local Correlation Integral (**LOCI**)\n* Lightweight Online Detector of Anomalies (**LODA**)\n* Locally Selective Combination of Parallel Outlier Ensembles (**LSCP**)\n* GNN-based Anomaly Detection Method (**LUNAR**)\n* Median Absolute Deviation (**MAD**)\n* Minimum Covariance Determinant (**MCD**)\n* One-Class SVM (**OCSVM**)\n* Principal Component Analysis (**PCA**)\n* Quasi-Monte Carlo Discrepancy Outlier Detection (**QMCD**)\n* Rotation-based Outlier Detection (**ROD**)\n* Subspace Outlier Detection (**SOD**)\n* Scalable Unsupervised Outlier Detection (**SUOD**)\n\n# Contact\n**Bug reporting:** [https://github.com/OliverHennhoefer/nonconform/issues](https://github.com/OliverHennhoefer/nonconform/issues)\n",
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