text-explainability 0.4.1

Generic explainability architecture for text machine learning models

A generic explainability architecture for explaining text machine learning models.

Marcel Robeer, 2021

Installation

Method	Instructions
pip	Install from PyPI via pip3 install text_explainability.
Local	Clone this repository and install via pip3 install -e . or locally run python3 setup.py install.

Example usage

See example_usage.md to see an example of how the package can be used, or run the lines in example_usage.py to do explore it interactively.

Explanation methods included

text_explainability includes methods for model-agnostic local explanation and global explanation. Each of these methods can be fully customized to fit the explainees' needs.

Type	Explanation method	Description	Paper/link
Local explanation	LIME	Calculate feature attribution with Local Intepretable Model-Agnostic Explanations (LIME).	[Ribeiro2016], interpretable-ml/lime
	KernelSHAP	Calculate feature attribution with Shapley Additive Explanations (SHAP).	[Lundberg2017], interpretable-ml/shap
	LocalTree	Fit a local decision tree around a single decision.	[Guidotti2018]
	FoilTree	Fit a local contrastive/counterfactual decision tree around a single decision.	[Robeer2018]
Global explanation	TokenFrequency	Show the top-k number of tokens for each ground-truth or predicted label.
	TokenInformation	Show the top-k token mutual information for a dataset or model.	wikipedia/mutual_information
	KMedoids	Embed instances and find top-n prototypes (can also be performed for each label using LabelwiseKMedoids).	interpretable-ml/prototypes
	MMDCritic	Embed instances and find top-n prototypes and top-n criticisms (can also be performed for each label using LabelwiseMMDCritic).	[Kim2016], interpretable-ml/prototypes

Releases

text_explainability is officially released through PyPI.

See CHANGELOG.md for a full overview of the changes for each version.

Maintenance

Contributors

• Marcel Robeer (@m.j.robeer)
• Michiel Bron (@mpbron-phd)

Todo

Tasks yet to be done:

• Implement local post-hoc explanations:
  • Implement Anchors
• Implement global post-hoc explanations:
  • Representative subset
• Add support for regression models
• More complex data augmentation
  • Top-k replacement (e.g. according to LM / WordNet)
  • Tokens to exclude from being changed
  • Bag-of-words style replacements
• Add rule-based return type
• Write more tests