alibi 0.2.0

Algorithms for monitoring and explaining machine learning models

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

Goals

• 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

Installation

Alibi can be installed from PyPI:

pip install alibi

Examples

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: