Example-based

Release v1.4.0 Introduce the Example-based module

This module introduces example-based methods from four families of methods. Namely:

• Similar Examples
• Semi-factuals
• Counterfactuals
• Prototypes

The API

The API is common for the four families, and it has three steps:

• Construct a projection function with the model.
• Initialize the method with a dataset, projection, and other parameters.
• Explain given samples with a local explanation.

projection = ProjectionMethod(model)

explainer = ExampleMethod(
    cases_dataset=cases_dataset, 
    k=k,
    projection=projection,
    case_returns=case_returns,
    distance=distance,
)

examples = explainer.explain(inputs, targets)

It works for TensorFlow and PyTorch without additional code. To have more information, please look at the documentation or to the tutorial.

Other modifications

• Xplique is limited to TensorFlow < 2.16 as this version of tf introduces many modifications.
• The features visualization module now supports grey-scale images.