Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The initial focus on the library is on black-box, instance based model explanations.

• Documentation

• Provide high quality reference implementations of black-box ML model explanation algorithms
• Define a consistent API for interpretable ML methods
• Support multiple use cases (e.g. tabular, text and image data classification, regression)
• Implement the latest model explanation, concept drift, algorithmic bias detection and other ML model monitoring and interpretation methods

Alibi can be installed from PyPI:

pip install alibi

This will install alibi with all its dependencies:

  beautifulsoup4
  numpy
  Pillow
  pandas
  requests
  scikit-learn
  spacy
  scikit-image
  tensorflow

To run all the example notebooks, you may additionally run pip install alibi[examples] which will install the following:

  seaborn
  Keras

Anchor method applied to the InceptionV3 model trained on ImageNet:

Prediction: Persian Cat	Anchor explanation

Contrastive Explanation method applied to a CNN trained on MNIST:

Prediction: 4	Pertinent Negative: 9	Pertinent Positive: 4

Trust scores applied to a softmax classifier trained on MNIST: