| Name | nonconform JSON |
| Version |
0.9.12
JSON |
| download |
| home_page | None |
| Summary | Conformal Anomaly Detection |
| upload_time | 2025-07-24 09:19:12 |
| 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
modification, are permitted provided that the following conditions are met:
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3. Neither the name of the copyright holder nor the names of its
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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| keywords |
anomaly detection
conformal anomaly detection
conformal inference
false discovery rate
uncertainty quantification
|
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**nonconform** is a Python library that enhances anomaly detection by providing uncertainty quantification. It acts as a wrapper around most detectors from the popular [*PyOD*](https://pyod.readthedocs.io/en/latest/) library (see [Supported Estimators](#supported-estimators)). By leveraging one-class classification principles and **conformal inference**, **nonconform** enables **statistically rigorous anomaly detection**.
# Key Features
* **Uncertainty Quantification:** Go beyond simple anomaly scores; get statistically valid _p_-values.
* **Error Control:** Reliably control metrics like the False Discovery Rate (FDR).
* **Broad PyOD Compatibility:** Works with a wide range of PyOD estimators (see [Supported Estimators](#supported-estimators)).
* **Flexible Strategies:** Implements various conformal strategies like Split-Conformal and Bootstrap-after-Jackknife+ (JaB+).
# Getting Started
```sh
pip install nonconform
```
_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 tools (black, ruff, pre-commit)
- `pip install nonconform[docs]` - Includes documentation building tools (sphinx, furo, etc.)
- `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._
## Split-Conformal (also _Inductive_) Approach
Using a _Gaussian Mixture Model_ on the _Shuttle_ dataset:
> **Note:** The examples below use the built-in datasets. Install with `pip install nonconform[data]` to run these examples.
```python
from pyod.models.gmm import GMM
from scipy.stats import false_discovery_control
from nonconform.strategy import Split
from nonconform.estimation import StandardConformalDetector
from nonconform.utils.data import load_shuttle
from nonconform.utils.stat import false_discovery_rate, statistical_power
x_train, x_test, y_test = load_shuttle(setup=True)
ce = StandardConformalDetector(
detector=GMM(),
strategy=Split(calib_size=1_000)
)
ce.fit(x_train)
estimates = ce.predict(x_test)
decisions = false_discovery_control(estimates, method='bh') <= 0.2
print(f"Empirical FDR: {false_discovery_rate(y=y_test, y_hat=decisions)}")
print(f"Empirical Power: {statistical_power(y=y_test, y_hat=decisions)}")
```
Output:
```text
Empirical FDR: 0.108
Empirical Power: 0.99
```
# Advanced Usage
## Bootstrap-after-Jackknife+ (JaB+)
The `BootstrapConformal()` strategy allows to set 2 of the 3 parameters `resampling_ratio`, `n_boostraps` and `n_calib`.
For either combination, the remaining parameter will be filled automatically. This allows exact control of the
calibration procedure when using a bootstrap strategy.
```python
from pyod.models.iforest import IForest
from scipy.stats import false_discovery_control
from nonconform.estimation import StandardConformalDetector
from nonconform.strategy import Bootstrap
from nonconform.utils.data import load_shuttle
from nonconform.utils.stat import false_discovery_rate, statistical_power
x_train, x_test, y_test = load_shuttle(setup=True)
ce = StandardConformalDetector(
detector=IForest(behaviour="new"),
strategy=Bootstrap(resampling_ratio=0.99, n_bootstraps=20, plus=True)
)
ce.fit(x_train)
estimates = ce.predict(x_test)
decisions = false_discovery_control(estimates, method='bh') <= 0.1
print(f"Empirical FDR: {false_discovery_rate(y=y_test, y_hat=decisions)}")
print(f"Empirical Power: {statistical_power(y=y_test, y_hat=decisions)}")
```
Output:
```text
Empirical FDR: 0.067
Empirical Power: 0.98
```
## Weighted Conformal Anomaly Detection
The statistical validity of conformal anomaly detection depends on data *exchangability* (weaker than i.i.d.). This assumption can be slightly relaxed by computing weighted conformal _p_-values.
```python
from pyod.models.iforest import IForest
from scipy.stats import false_discovery_control
from nonconform.utils.data import load_shuttle
from nonconform.estimation import WeightedConformalDetector
from nonconform.strategy import Split
from nonconform.utils.stat import false_discovery_rate, statistical_power
x_train, x_test, y_test = load_shuttle(setup=True)
model = IForest(behaviour="new")
strategy = Split(calib_size=1_000)
ce = WeightedConformalDetector(detector=model, strategy=strategy)
ce.fit(x_train)
estimates = ce.predict(x_test)
decisions = false_discovery_control(estimates, method='bh') <= 0.1
print(f"Empirical FDR: {false_discovery_rate(y=y_test, y_hat=decisions)}")
print(f"Empirical Power: {statistical_power(y=y_test, y_hat=decisions)}")
```
Output:
```text
Empirical FDR: 0.077
Empirical Power: 0.96
```
# Citation
If you find this repository useful for your research, please cite 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}
}
```
##### Model-free selective inference under covariate shift via weighted conformal p-values
```text
@inproceedings{Jin2023,
title = {Model-free selective inference under covariate shift via weighted conformal p-values},
author = {Ying Jin and Emmanuel J. Cand{\`e}s},
year = 2023,
url = {https://api.semanticscholar.org/CorpusID:259950903}
}
```
# Supported Estimators
The package only supports anomaly estimators that are suitable for unsupervised one-class classification. As respective
detectors are therefore exclusively fitted on *normal* (or *non-anomalous*) data, parameters like *threshold* are internally
set to the smallest possible 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)
Raw data
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"description": "**nonconform** is a Python library that enhances anomaly detection by providing uncertainty quantification. It acts as a wrapper around most detectors from the popular [*PyOD*](https://pyod.readthedocs.io/en/latest/) library (see [Supported Estimators](#supported-estimators)). By leveraging one-class classification principles and **conformal inference**, **nonconform** enables **statistically rigorous anomaly detection**.\n\n# Key Features\n\n* **Uncertainty Quantification:** Go beyond simple anomaly scores; get statistically valid _p_-values.\n* **Error Control:** Reliably control metrics like the False Discovery Rate (FDR).\n* **Broad PyOD Compatibility:** Works with a wide range of PyOD estimators (see [Supported Estimators](#supported-estimators)).\n* **Flexible Strategies:** Implements various conformal strategies like Split-Conformal and Bootstrap-after-Jackknife+ (JaB+).\n\n# Getting Started\n\n```sh\npip install nonconform\n```\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 tools (black, ruff, pre-commit)\n- `pip install nonconform[docs]` - Includes documentation building tools (sphinx, furo, etc.)\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## Split-Conformal (also _Inductive_) Approach\n\nUsing a _Gaussian Mixture Model_ on the _Shuttle_ dataset:\n\n> **Note:** The examples below use the built-in datasets. Install with `pip install nonconform[data]` to run these examples.\n\n```python\nfrom pyod.models.gmm import GMM\nfrom scipy.stats import false_discovery_control\n\nfrom nonconform.strategy import Split\nfrom nonconform.estimation import StandardConformalDetector\nfrom nonconform.utils.data import load_shuttle\nfrom nonconform.utils.stat import false_discovery_rate, statistical_power\n\nx_train, x_test, y_test = load_shuttle(setup=True)\n\nce = StandardConformalDetector(\n detector=GMM(),\n strategy=Split(calib_size=1_000)\n)\n\nce.fit(x_train)\nestimates = ce.predict(x_test)\n\ndecisions = false_discovery_control(estimates, method='bh') <= 0.2\n\nprint(f\"Empirical FDR: {false_discovery_rate(y=y_test, y_hat=decisions)}\")\nprint(f\"Empirical Power: {statistical_power(y=y_test, y_hat=decisions)}\")\n```\n\nOutput:\n```text\nEmpirical FDR: 0.108\nEmpirical Power: 0.99\n```\n\n# Advanced Usage\n\n## Bootstrap-after-Jackknife+ (JaB+)\n\nThe `BootstrapConformal()` strategy allows to set 2 of the 3 parameters `resampling_ratio`, `n_boostraps` and `n_calib`.\nFor either combination, the remaining parameter will be filled automatically. This allows exact control of the\ncalibration procedure when using a bootstrap strategy.\n\n```python\nfrom pyod.models.iforest import IForest\nfrom scipy.stats import false_discovery_control\n\nfrom nonconform.estimation import StandardConformalDetector\nfrom nonconform.strategy import Bootstrap\nfrom nonconform.utils.data import load_shuttle\nfrom nonconform.utils.stat import false_discovery_rate, statistical_power\n\nx_train, x_test, y_test = load_shuttle(setup=True)\n\nce = StandardConformalDetector(\n detector=IForest(behaviour=\"new\"),\n strategy=Bootstrap(resampling_ratio=0.99, n_bootstraps=20, plus=True)\n)\n\nce.fit(x_train)\nestimates = ce.predict(x_test)\n\ndecisions = false_discovery_control(estimates, method='bh') <= 0.1\n\nprint(f\"Empirical FDR: {false_discovery_rate(y=y_test, y_hat=decisions)}\")\nprint(f\"Empirical Power: {statistical_power(y=y_test, y_hat=decisions)}\")\n```\n\nOutput:\n```text\nEmpirical FDR: 0.067\nEmpirical Power: 0.98\n```\n\n## Weighted Conformal Anomaly Detection\n\nThe statistical validity of conformal anomaly detection depends on data *exchangability* (weaker than i.i.d.). This assumption can be slightly relaxed by computing weighted conformal _p_-values.\n\n```python\nfrom pyod.models.iforest import IForest\nfrom scipy.stats import false_discovery_control\n\nfrom nonconform.utils.data import load_shuttle\nfrom nonconform.estimation import WeightedConformalDetector\nfrom nonconform.strategy import Split\nfrom nonconform.utils.stat import false_discovery_rate, statistical_power\n\nx_train, x_test, y_test = load_shuttle(setup=True)\n\nmodel = IForest(behaviour=\"new\")\nstrategy = Split(calib_size=1_000)\n\nce = WeightedConformalDetector(detector=model, strategy=strategy)\nce.fit(x_train)\nestimates = ce.predict(x_test)\n\ndecisions = false_discovery_control(estimates, method='bh') <= 0.1\n\nprint(f\"Empirical FDR: {false_discovery_rate(y=y_test, y_hat=decisions)}\")\nprint(f\"Empirical Power: {statistical_power(y=y_test, y_hat=decisions)}\")\n```\n\nOutput:\n```text\nEmpirical FDR: 0.077\nEmpirical Power: 0.96\n```\n\n# Citation\n\nIf you find this repository useful for your research, please cite 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##### Model-free selective inference under covariate shift via weighted conformal p-values\n```text\n@inproceedings{Jin2023,\n\ttitle = {Model-free selective inference under covariate shift via weighted conformal p-values},\n\tauthor = {Ying Jin and Emmanuel J. Cand{\\`e}s},\n\tyear = 2023,\n\turl = {https://api.semanticscholar.org/CorpusID:259950903}\n}\n```\n\n# Supported Estimators\n\nThe package only supports anomaly estimators that are suitable for unsupervised one-class classification. As respective\ndetectors are therefore exclusively fitted on *normal* (or *non-anomalous*) data, parameters like *threshold* are internally\nset to the smallest possible 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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