Python Outlier Detection (PyOD) V2

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 <https://www.arxiv.org/abs/2412.12154>) [#Chen2024PyOD], 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.

Additional Resources:

• NLP Anomaly Detection: NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>_ provides both NLP anonaly detection datasets and algorithms
• Time-series Outlier Detection: TODS <https://github.com/datamllab/tods>_
• Graph Outlier Detection: PyGOD <https://pygod.org/>_
• Performance Comparison & Datasets: Our 45-page anomaly detection benchmark paper <https://openreview.net/forum?id=foA_SFQ9zo0>_ and ADBench <https://github.com/Minqi824/ADBench>_, comparing 30 algorithms on 57 datasets
• PyOD on Distributed Systems: PyOD on Databricks <https://www.databricks.com/blog/2023/03/13/unsupervised-outlier-detection-databricks.html>_
• Learn More: Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>_

Check out our latest research in 2025 on LLM-based anomaly detection [#Yang2024ad]: AD-LLM: Benchmarking Large Language Models for Anomaly Detection <https://arxiv.org/abs/2412.11142>.

---

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 <https://en.wikipedia.org/wiki/Anomaly_detection>_ or Anomaly Detection <https://en.wikipedia.org/wiki/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 <https://pepy.tech/project/pyod>. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>, KDnuggets <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>, and Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>.

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 <https://github.com/numba/numba>_ and joblib <https://github.com/joblib/joblib>_ for JIT compilation and parallel processing.
• Fast Training & Prediction, achieved through the SUOD framework [#Zhao2021SUOD]_.

Outlier Detection with 5 Lines of Code:

.. code-block:: python

# 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

Selecting the Right Algorithm: Unsure where to start? Consider these robust and interpretable options:

• ECOD <https://github.com/yzhao062/pyod/blob/master/examples/ecod_example.py>_: Example of using ECOD for outlier detection
• Isolation Forest <https://github.com/yzhao062/pyod/blob/master/examples/iforest_example.py>_: Example of using Isolation Forest for outlier detection

Alternatively, explore MetaOD <https://github.com/yzhao062/MetaOD>_ for a data-driven approach.

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 <https://arxiv.org/abs/2412.12154>_ 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 <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>_ is published in Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/>_ (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 ADBench: Anomaly Detection Benchmark Paper <https://arxiv.org/abs/2206.09426>_ and ADGym: Design Choices for Deep Anomaly Detection <https://arxiv.org/abs/2309.15376>_::

@article{han2022adbench,
    title={Adbench: Anomaly detection benchmark},
    author={Han, Songqiao and Hu, Xiyang and Huang, Hailiang and Jiang, Minqi and Zhao, Yue},
    journal={Advances in Neural Information Processing Systems},
    volume={35},
    pages={32142--32159},
    year={2022}
}

@article{jiang2023adgym,
    title={ADGym: Design Choices for Deep Anomaly Detection},
    author={Jiang, Minqi and Hou, Chaochuan and Zheng, Ao and Han, Songqiao and Huang, Hailiang and Wen, Qingsong and Hu, Xiyang and Zhao, Yue},
    journal={Advances in Neural Information Processing Systems},
    volume={36},
    year={2023}
}

Table of Contents:

• Installation <#installation>_
• API Cheatsheet & Reference <#api-cheatsheet--reference>_
• ADBench Benchmark and Datasets <#adbench-benchmark-and-datasets>_
• Model Save & Load <#model-save--load>_
• Fast Train with SUOD <#fast-train-with-suod>_
• Thresholding Outlier Scores <#thresholding-outlier-scores>_
• Implemented Algorithms <#implemented-algorithms>_
• Quick Start for Outlier Detection <#quick-start-for-outlier-detection>_
• How to Contribute <#how-to-contribute>_
• Inclusion Criteria <#inclusion-criteria>_

---

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:

.. code-block:: bash

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

.. code-block:: bash

conda install -c conda-forge pyod

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

.. code-block:: bash

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