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Algorithms for outlier detection, concept drift and metrics.
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alibi_detect
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README.md

Build Status Documentation Status Python version PyPI version GitHub Licence Slack channel

alibi-detect is an open source Python library focused on outlier, adversarial and concept drift detection. The package aims to cover both online and offline detectors for tabular data, images and time series. The outlier detection methods should allow the user to identify global, contextual and collective outliers.

Installation

alibi-detect can be installed from PyPI:

pip install alibi-detect

This will install alibi-detect with all its dependencies:

  creme
  fbprophet
  matplotlib
  numpy
  pandas
  scipy
  scikit-learn
  tensorflow>=2
  tensorflow_probability>=0.8

Supported algorithms

Outlier Detection

The following table shows the advised use cases for each algorithm. The column Feature Level indicates whether the outlier scoring and detection can be done and returned at the feature level, e.g. per pixel for an image:

Detector Tabular Image Time Series Text Categorical Features Online Feature Level
Isolation Forest
Mahalanobis Distance
VAE
AEGMM
VAEGMM
Prophet
Spectral Residual

Adversarial Detection

Advised use cases:

Detector Tabular Image Time Series Text Categorical Features Online Feature Level
Adversarial VAE

Integrations

The integrations folder contains various wrapper tools to allow the alibi-detect algorithms to be used in production machine learning systems with examples on how to deploy outlier and adversarial detectors with KFServing.

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