Deployment & Documentation & Stats & License

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

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

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)

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.

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.

• 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.

PyOD toolkit consists of four major functional groups:

(i) Individual Detection Algorithms :

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:

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):