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Python Streaming Anomaly Detection (PySAD)

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PySAD is an open-source python framework for anomaly detection on streaming multivariate data.

Documentation <https://pysad.readthedocs.io/en/latest/>__

Features

Online Anomaly Detection ^^^^^^^^^^^^^^^^^^^^^^^^

PySAD provides methods for online/sequential anomaly detection, i.e. anomaly detection on streaming data, where model updates itself as a new instance arrives.

Resource-Efficient ^^^^^^^^^^^^^^^^^^

Streaming methods efficiently handle the limitied memory and processing time requirements of the data streams so that they can be used in near real-time. The methods can only store an instance or a small window of recent instances.

Complete ^^^^^^^^

PySAD contains stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, probability calibrators and more. In addition to streaming models, PySAD also provides integrations for batch anomaly detectors of the PyOD <https://github.com/yzhao062/pyod/>_ so that they can be used in the streaming setting.

Comprehensive ^^^^^^^^^^^^^

PySAD serves models that are specifically designed for both univariate and multivariate data. Furthermore, one can experiment via PySAD in supervised, semi-supervised and unsupervised setting.

User Friendly ^^^^^^^^^^^^^

Users with any experience level can easily use PySAD. One can easily design experiments and combine the tools in the framework. Moreover, the existing methods in PySAD are easy to extend.

Free and Open Source Software (FOSS) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

PySAD is distributed under BSD License 2.0 <https://github.com/selimfirat/pysad/blob/master/LICENSE>_ and favors FOSS principles.

Installation

The PySAD framework can be installed via:

.. code-block:: bash

pip install -U pysad

Alternatively, you can install the library directly using the source code in Github repository by:

.. code-block:: bash

git clone https://github.com/selimfirat/pysad.git
cd pysad
pip install .

Required Dependencies:

• Python 3.8
• numpy==1.23.5
• scikit-learn>=1.3.0
• scipy==1.10.0
• pyod==1.1.0
• combo==0.1.3

Optional Dependencies:

• rrcf==0.4.3 (Only required for pysad.models.robust_random_cut_forest.RobustRandomCutForest)
• PyNomaly==0.3.3 (Only required for pysad.models.loop.StreamLocalOutlierProbability)
• mmh3==2.5.1 (Only required for pysad.models.xstream.xStream)
• pandas==2.0.3 (Only required for pysad.utils.pandas_streamer.PandasStreamer)

Quick Links

• Github Repository <https://github.com/selimfirat/pysad/>_

• Documentation <http://pysad.readthedocs.io/>__

• PyPI Package <https://pypi.org/project/pysad>_

• Travis CI <https://travis-ci.com/github/selimfirat/pysad>_

• Azure Pipelines <https://dev.azure.com/selimfirat/pysad/>_

• Circle CI <https://circleci.com/gh/selimfirat/pysad/>_

• Appveyor <https://ci.appveyor.com/project/selimfirat/pysad/branch/master>_

• Coveralls <https://coveralls.io/github/selimfirat/pysad?branch=master>_

• License <https://github.com/selimfirat/pysad/blob/master/LICENSE>_

Versioning

Semantic versioning <http://semver.org/>_ is used for this project.

License

This project is licensed under the BSD License 2.0 <https://github.com/selimfirat/pysad/blob/master/LICENSE>_.

Citing PySAD

If you use PySAD for a scientific publication, please cite the following paper:

.. code-block::

@article{pysad,
  title={PySAD: A Streaming Anomaly Detection Framework in Python},
  author={Yilmaz, Selim F and Kozat, Suleyman S},
  journal={arXiv preprint arXiv:2009.02572},
  year={2020}
}