pyod 2.1.0

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

Deployment & Documentation & Stats & License

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Read Me First

Welcome to PyOD, a well-developed and easy-to-use Python library for detecting anomalies in multivariate data. Whether you are working with a small-scale project or large datasets, PyOD provides a range of algorithms to fit your needs.

PyOD Version 2 is now available (Paper) [12], featuring:

• Expanded Deep Learning Support: Integrates 12 modern neural models into a single PyTorch-based framework, bringing the total number of outlier detection methods to 45.

• Enhanced Performance and Ease of Use: Models are optimized for efficiency and consistent performance across different datasets.

• LLM-based Model Selection: Automated model selection guided by a large language model reduces manual tuning and assists users who may have limited experience with outlier detection.

• Multi-Modal Detection via EmbeddingOD: Chain foundation model encoders (sentence-transformers, OpenAI, HuggingFace) with any PyOD detector for text and image anomaly detection. See EmbeddingOD example.

PyOD Ecosystem & Resources: NLP-ADBench (NLP anomaly detection) | TODS (time-series) | PyGOD (graph) | ADBench (benchmark) | AD-LLM (LLM-based AD) [50] | Resources

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About PyOD

PyOD, established in 2017, has become a go-to Python library for detecting anomalous/outlying objects in multivariate data. This exciting yet challenging field is commonly referred to as Outlier Detection or Anomaly Detection.

PyOD includes more than 50 detection algorithms, from classical LOF (SIGMOD 2000) to the cutting-edge ECOD and DIF (TKDE 2022 and 2023). Since 2017, PyOD has been successfully used in numerous academic research projects and commercial products with more than 26 million downloads. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including Analytics Vidhya, KDnuggets, and Towards Data Science.

PyOD is featured for:

• Unified, User-Friendly Interface across various algorithms.

• Wide Range of Models, from classic techniques to the latest deep learning methods in PyTorch.

• High Performance & Efficiency, leveraging numba and joblib for JIT compilation and parallel processing.

• Fast Training & Prediction, achieved through the SUOD framework [55].

Outlier Detection with 5 Lines of Code:

# Example: Training an ECOD detector
from pyod.models.ecod import ECOD
clf = ECOD()
clf.fit(X_train)
y_train_scores = clf.decision_scores_  # Outlier scores for training data
y_test_scores = clf.decision_function(X_test)  # Outlier scores for test data

Text Anomaly Detection with EmbeddingOD (pip install pyod sentence-transformers):

from pyod.models.embedding import EmbeddingOD
clf = EmbeddingOD(encoder='all-MiniLM-L6-v2', detector='KNN')
clf.fit(train_texts)                          # list of strings
scores = clf.decision_function(test_texts)    # anomaly scores
labels = clf.predict(test_texts)              # binary labels

# Or use a preset:
clf = EmbeddingOD.for_text(quality='fast')    # MiniLM + KNN

Image detection requires additional packages (pip install transformers torch). See EmbeddingOD example for details.

Selecting the Right Algorithm: Start with ECOD or Isolation Forest for tabular data, EmbeddingOD for text/image, or MetaOD for data-driven selection.

Citing PyOD:

If you use PyOD in a scientific publication, we would appreciate citations to the following paper(s):

PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection is available as a preprint. If you use PyOD in a scientific publication, we would appreciate citations to the following paper:

@inproceedings{chen2025pyod,
  title={Pyod 2: A python library for outlier detection with llm-powered model selection},
  author={Chen, Sihan and Qian, Zhuangzhuang and Siu, Wingchun and Hu, Xingcan and Li, Jiaqi and Li, Shawn and Qin, Yuehan and Yang, Tiankai and Xiao, Zhuo and Ye, Wanghao and others},
  booktitle={Companion Proceedings of the ACM on Web Conference 2025},
  pages={2807--2810},
  year={2025}
}

PyOD paper is published in Journal of Machine Learning Research (JMLR) (MLOSS track).:

@article{zhao2019pyod,
    author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
    title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
    journal = {Journal of Machine Learning Research},
    year    = {2019},
    volume  = {20},
    number  = {96},
    pages   = {1-7},
    url     = {http://jmlr.org/papers/v20/19-011.html}
}

or:

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.

For a broader perspective on anomaly detection, see our NeurIPS papers on ADBench [17] and ADGym.

Table of Contents:

• Installation

• API Cheatsheet & Reference

• ADBench Benchmark and Datasets

• Additional Topics (Model Save/Load, SUOD, Thresholding)

• Implemented Algorithms

• Quick Start for Outlier Detection

• How to Contribute

• Inclusion Criteria

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Installation

PyOD is designed for easy installation using either pip or conda. We recommend using the latest version of PyOD due to frequent updates and enhancements:

pip install pyod            # normal install
pip install --upgrade pyod  # or update if needed

conda install -c conda-forge pyod

Alternatively, you can clone and run the setup.py file:

git clone https://github.com/yzhao062/pyod.git
cd pyod
pip install .

Required Dependencies:

• Python 3.8 or higher

• joblib

• matplotlib

• numpy>=1.19

• numba>=0.51

• scipy>=1.5.1

• scikit_learn>=0.22.0

Optional Dependencies (see details below):

• combo (optional, required for models/combination.py and FeatureBagging)

• pytorch (optional, required for AutoEncoder, and other deep learning models)

• suod (optional, required for running SUOD model)

• xgboost (optional, required for XGBOD)

• pythresh (optional, required for thresholding)

• sentence-transformers (optional, required for EmbeddingOD text detection)

• openai (optional, required for EmbeddingOD with OpenAI embeddings)

• transformers and torch (optional, required for EmbeddingOD image detection and HuggingFace encoder)

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API Cheatsheet & Reference

The full API Reference is available at PyOD Documentation. Below is a quick cheatsheet for all detectors:

• fit(X): Fit the detector. The parameter y is ignored in unsupervised methods.

• decision_function(X): Predict raw anomaly scores for X using the fitted detector.

• predict(X): Determine whether a sample is an outlier or not as binary labels using the fitted detector.

• predict_proba(X): Estimate the probability of a sample being an outlier using the fitted detector.

• predict_confidence(X): Assess the model’s confidence on a per-sample basis (applicable in predict and predict_proba) [38].

• predict_with_rejection(X): Allow the detector to reject (i.e., abstain from making) highly uncertain predictions (output = -2) [39].

Key Attributes of a fitted model:

• decision_scores_: Outlier scores of the training data. Higher scores typically indicate more abnormal behavior. Outliers usually have higher scores.

• labels_: Binary labels of the training data, where 0 indicates inliers and 1 indicates outliers/anomalies.

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ADBench Benchmark and Datasets

We just released a 45-page, the most comprehensive ADBench: Anomaly Detection Benchmark [17]. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 57 benchmark datasets.

The organization of ADBench is provided below:

For a simpler visualization, we make the comparison of selected models via compare_all_models.py.

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Additional Topics

• Model Save & Load: Use joblib or pickle for saving and loading PyOD models. See example.

• Fast Train with SUOD: Accelerate training and prediction with the SUOD framework [55]. See example.

• Thresholding Outlier Scores: Data-driven approaches for setting contamination levels via PyThresh.

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Implemented Algorithms

PyOD toolkit consists of four major functional groups:

(i) Individual Detection Algorithms :

Type	Abbr	Algorithm	Year	Ref
Probabilistic	ECOD	Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions	2022	[31]
Probabilistic	ABOD	Angle-Based Outlier Detection	2008	[24]
Probabilistic	FastABOD	Fast Angle-Based Outlier Detection using approximation	2008	[24]
Probabilistic	COPOD	COPOD: Copula-Based Outlier Detection	2020	[30]
Probabilistic	MAD	Median Absolute Deviation (MAD)	1993	[21]
Probabilistic	SOS	Stochastic Outlier Selection	2012	[22]
Probabilistic	QMCD	Quasi-Monte Carlo Discrepancy outlier detection	2001	[13]
Probabilistic	KDE	Outlier Detection with Kernel Density Functions	2007	[26]
Probabilistic	Sampling	Rapid distance-based outlier detection via sampling	2013	[46]
Probabilistic	GMM	Probabilistic Mixture Modeling for Outlier Analysis		[1] [Ch.2]
Linear Model	PCA	Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)	2003	[45]
Linear Model	KPCA	Kernel Principal Component Analysis	2007	[20]
Linear Model	MCD	Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)	1999	[18] [41]
Linear Model	CD	Use Cook’s distance for outlier detection	1977	[11]
Linear Model	OCSVM	One-Class Support Vector Machines	2001	[44]
Linear Model	LMDD	Deviation-based Outlier Detection (LMDD)	1996	[6]
Proximity-Based	LOF	Local Outlier Factor	2000	[8]
Proximity-Based	COF	Connectivity-Based Outlier Factor	2002	[47]
Proximity-Based	(Incremental) COF	Memory Efficient Connectivity-Based Outlier Factor (slower but reduce storage complexity)	2002	[47]
Proximity-Based	CBLOF	Clustering-Based Local Outlier Factor	2003	[19]
Proximity-Based	LOCI	LOCI: Fast outlier detection using the local correlation integral	2003	[36]
Proximity-Based	HBOS	Histogram-based Outlier Score	2012	[14]
Proximity-Based	HDBSCAN	Density-based clustering based on hierarchical density estimates	2013	[10]
Proximity-Based	kNN	k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score)	2000	[40]
Proximity-Based	AvgKNN	Average kNN (use the average distance to k nearest neighbors as the outlier score)	2002	[5]
Proximity-Based	MedKNN	Median kNN (use the median distance to k nearest neighbors as the outlier score)	2002	[5]
Proximity-Based	SOD	Subspace Outlier Detection	2009	[25]
Proximity-Based	ROD	Rotation-based Outlier Detection	2020	[4]
Outlier Ensembles	IForest	Isolation Forest	2008	[32]
Outlier Ensembles	INNE	Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles	2018	[7]
Outlier Ensembles	DIF	Deep Isolation Forest for Anomaly Detection	2023	[49]
Outlier Ensembles	FB	Feature Bagging	2005	[27]
Outlier Ensembles	LSCP	LSCP: Locally Selective Combination of Parallel Outlier Ensembles	2019	[54]
Outlier Ensembles	XGBOD	Extreme Boosting Based Outlier Detection (Supervised)	2018	[53]
Outlier Ensembles	LODA	Lightweight On-line Detector of Anomalies	2016	[37]
Outlier Ensembles	SUOD	SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection (Acceleration)	2021	[55]
Neural Networks	AutoEncoder	Fully connected AutoEncoder (use reconstruction error as the outlier score)		[1] [Ch.3]
Neural Networks	VAE	Variational AutoEncoder (use reconstruction error as the outlier score)	2013	[23]
Neural Networks	Beta-VAE	Variational AutoEncoder (all customized loss term by varying gamma and capacity)	2018	[9]
Neural Networks	SO_GAAL	Single-Objective Generative Adversarial Active Learning	2019	[33]
Neural Networks	MO_GAAL	Multiple-Objective Generative Adversarial Active Learning	2019	[33]
Neural Networks	DeepSVDD	Deep One-Class Classification	2018	[42]
Neural Networks	AnoGAN	Anomaly Detection with Generative Adversarial Networks	2017	[43]
Neural Networks	ALAD	Adversarially learned anomaly detection	2018	[52]
Neural Networks	AE1SVM	Autoencoder-based One-class Support Vector Machine	2019	[34]
Neural Networks	DevNet	Deep Anomaly Detection with Deviation Networks	2019	[35]
Graph-based	R-Graph	Outlier detection by R-graph	2017	[51]
Graph-based	LUNAR	LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks	2022	[15]
Embedding-based	EmbeddingOD	Multi-modal anomaly detection via foundation model embeddings (text, image)	2025	[28]

Ensemble methods (IForest, INNE, DIF, FB, LSCP, LODA, SUOD, XGBOD) are included in the table above. Score combination functions (average, maximization, AOM, MOA, median, majority vote) are in pyod.models.combination. See API docs for details.

(ii) Utility Functions:

Type	Name	Function
Data	generate_data	Synthesized data generation; normal data from multivariate Gaussian, outliers from uniform distribution
Data	generate_data_clusters	Synthesized data generation in clusters for more complex patterns
Evaluation	evaluate_print	Print ROC-AUC and Precision @ Rank n for a detector
Evaluation	precision_n_scores	Calculate Precision @ Rank n
Utility	get_label_n	Turn raw outlier scores into binary labels by assigning 1 to the top n scores
Stat	wpearsonr	Calculate the weighted Pearson correlation of two samples
Encoding	resolve_encoder	Resolve an encoder from a string name, BaseEncoder instance, or callable
Encoding	SentenceTransformerEncoder	Encode text via sentence-transformers models (e.g., MiniLM, mpnet)
Encoding	OpenAIEncoder	Encode text via OpenAI Embeddings API (text-embedding-3-small/large)
Encoding	HuggingFaceEncoder	Encode text or images via HuggingFace transformers (BERT, DINOv2, CLIP)

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Quick Start for Outlier Detection

PyOD has been well acknowledged by the machine learning community with a few featured posts and tutorials.

Analytics Vidhya: An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library

KDnuggets: Intuitive Visualization of Outlier Detection Methods, An Overview of Outlier Detection Methods from PyOD

Towards Data Science: Anomaly Detection for Dummies

“examples/knn_example.py” demonstrates the basic API of using kNN detector. It is noted that the API across all other algorithms are consistent/similar.

More detailed instructions for running examples can be found in examples directory.

1. Initialize a kNN detector, fit the model, and make the prediction.

   from pyod.models.knn import KNN   # kNN detector
   
   # train kNN detector
   clf_name = 'KNN'
   clf = KNN()
   clf.fit(X_train)
   
   # get the prediction label and outlier scores of the training data
   y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
   y_train_scores = clf.decision_scores_  # raw outlier scores
   
   # get the prediction on the test data
   y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
   y_test_scores = clf.decision_function(X_test)  # outlier scores
   
   # it is possible to get the prediction confidence as well
   y_test_pred, y_test_pred_confidence = clf.predict(X_test, return_confidence=True)  # outlier labels (0 or 1) and confidence in the range of [0,1]

2. Evaluate the prediction by ROC and Precision @ Rank n (p@n).

   from pyod.utils.data import evaluate_print
   
   # evaluate and print the results
   print("\nOn Training Data:")
   evaluate_print(clf_name, y_train, y_train_scores)
   print("\nOn Test Data:")
   evaluate_print(clf_name, y_test, y_test_scores)

3. See a sample output & visualization.

   On Training Data:
   KNN ROC:1.0, precision @ rank n:1.0
   
   On Test Data:
   KNN ROC:0.9989, precision @ rank n:0.9

   visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
       y_test_pred, show_figure=True, save_figure=False)

Visualization (knn_figure):

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