Metadata-Version: 2.1
Name: alibi-detect
Version: 0.1.0
Summary: Algorithms for outlier detection, concept drift and metrics.
Home-page: https://github.com/SeldonIO/alibi-detect
Author: Seldon Technologies Ltd.
Author-email: hello@seldon.io
License: Apache 2.0
Description: [alibi-detect](https://github.com/SeldonIO/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.
        
        *  [Documentation](https://docs.seldon.io/projects/alibi-detect)
        
        ## Installation
        
        alibi-detect can be installed from [PyPI](https://pypi.org/project/alibi-detect):
        ```bash
        pip install alibi-detect
        ```
        This will install `alibi-detect` with all its dependencies:
        ```bash
          creme
          fbprophet
          matplotlib
          numpy
          pandas
          scipy
          scikit-learn
          tensorflow>=2
          tensorflow_probability>=0.8
        ```
        
        ## Supported algorithms
        
        ### Outlier Detection
        
         - Isolation Forest ([FT Liu et al., 2008](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf))
           - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/methods/iforest.html)
           - Examples:
             [Network Intrusion](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/od_if_kddcup.html)
        
         - Mahalanobis Distance ([Mahalanobis, 1936](https://insa.nic.in/writereaddata/UpLoadedFiles/PINSA/Vol02_1936_1_Art05.pdf))
           - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/methods/mahalanobis.html)
           - Examples:
             [Network Intrusion](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/od_mahalanobis_kddcup.html)
        
         - Variational Auto-Encoder (VAE) ([Kingma et al., 2013](https://arxiv.org/abs/1312.6114))
           - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/methods/vae.html)
           - Examples:
             [Network Intrusion](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/od_vae_kddcup.html), [CIFAR10](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/od_vae_cifar10.html)
        
         - Auto-Encoding Gaussian Mixture Model (AEGMM) ([Zong et al., 2018](https://openreview.net/forum?id=BJJLHbb0-))
           - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/methods/aegmm.html)
           - Examples:
             [Network Intrusion](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/od_aegmm_kddcup.html)
        
         - Variational Auto-Encoding Gaussian Mixture Model (VAEGMM)
           - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/methods/vaegmm.html)
           - Examples:
             [Network Intrusion](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/od_aegmm_kddcup.html)
        
        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                | ✔       | ✔     |  ✘          |  ✘    |  ✘                   |  ✘     |  ✘            |
        
        
        ### Adversarial Detection
        
         - Adversarial Variational Auto-Encoder
           - [Documentation](https://docs.seldon.io/projects/alibi-detect/en/latest/methods/adversarialvae.html)
           - Examples:
             [MNIST](https://docs.seldon.io/projects/alibi-detect/en/latest/examples/ad_advvae_mnist.html)
        
        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](https://github.com/SeldonIO/alibi-detect/tree/master/integrations/samples/kfserving) on how to deploy outlier and adversarial detectors with [KFServing](https://www.kubeflow.org/docs/components/serving/kfserving/).
        
Platform: UNKNOWN
Requires-Python: >3.5.1
Description-Content-Type: text/markdown
Provides-Extra: examples
