This is the Linux app named Python Outlier Detection whose latest release can be downloaded as v1.0.6.zip. It can be run online in the free hosting provider OnWorks for workstations.
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Python Outlier Detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS.
- Unified APIs, detailed documentation, and interactive examples across various algorithms
- Advanced models, including classical ones from scikit-learn, latest deep learning methods, and emerging algorithms like COPOD
- Optimized performance with JIT and parallelization when possible, using numba and joblib
- Fast training & prediction with SUOD
- Compatible with both Python 2 & 3
- Individual detection algorithms
This is an application that can also be fetched from https://sourceforge.net/projects/python-outlier-detect.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.