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Higher Order OCclusion (hoocs)

Introduction

Hoocs implements a broad range of model-agnostic attributions

Recently, there has been increasing interest in more in-depth analysis of models. To meet this needs, the analysis of feature interactions is inevitable. Therefore, this package allows to calculate arbitrary higher-order explanations. Hoocs is extendable to other methods, which rely on marginalizing features in input space.

Installation

pip install hoocs

Implement new imputers

To enable reliable attributions, hooks enables simple incorporation of custom imputers for any kind of data modality. To add a new imputer, the user is requested to inherent from the abstract base Imputer class in hoocs.imputers.abstract_imputers.py. This class performs basic type checking and ensures a consistent interface.

Check out Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks if you are unsure how to choose the imputer for your specific use case.

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