https://github.com/selimfirat/pysad

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
https://github.com/selimfirat/pysad

anomaly anomaly-detection fraud-detection incremental-learning intrusion-detection machine-learning outlier-detection outliers python real-time streaming-data unsupervised-learning

Last synced: about 1 month ago
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Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Host: GitHub
URL: https://github.com/selimfirat/pysad
Owner: selimfirat
License: bsd-3-clause
Created: 2020-06-05T01:17:44.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-10-12T21:34:53.000Z (over 1 year ago)
Last Synced: 2024-12-06T19:33:21.274Z (4 months ago)
Topics: anomaly, anomaly-detection, fraud-detection, incremental-learning, intrusion-detection, machine-learning, outlier-detection, outliers, python, real-time, streaming-data, unsupervised-learning
Language: Python
Homepage:
Size: 400 KB
Stars: 243
Watchers: 7
Forks: 22
Open Issues: 3
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES.txt
- Contributing: docs/contributing.rst
- License: LICENSE

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awesome-AIOps - Anomaly Detection
awesome-TS-anomaly-detection - PySAD - Clause | ❌ (Anomaly Detection Software)

README

        .. image:: docs/logo.png

    :align: center

Python Streaming Anomaly Detection (PySAD)

==========================================

.. image:: https://img.shields.io/pypi/v/pysad

    :target: https://pypi.org/project/pysad/

    :alt: PyPI

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   :alt: GitHub release (latest by date)

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   :alt: Documentation status

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   :target: https://dev.azure.com/selimfirat/pysad/_build/latest?definitionId=2&branchName=master

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   :alt: Coverage Status

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   :alt: PyPI - Python Version

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   :alt: License

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

`Documentation `__

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 `_ 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 `_ 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 `_

* `Documentation `__

* `PyPI Package `_

* `Travis CI `_

* `Azure Pipelines `_

* `Circle CI `_

* `Appveyor `_

* `Coveralls `_

* `License `_

Versioning

==========

`Semantic versioning `_ is used for this project.

License

=======

This project is licensed under the `BSD License 2.0 `_.

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}

    }

ecosyste.ms

Data

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Indexes

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Experiments

Awesome

https://github.com/selimfirat/pysad

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README