text-explainability 0.7.1

Generic explainability architecture for text machine learning models

A generic explainability architecture for explaining text machine learning models

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text_explainability provides a generic architecture from which well-known state-of-the-art explainability approaches for text can be composed. This modular architecture allows components to be swapped out and combined, to quickly develop new types of explainability approaches for (natural language) text, or to improve a plethora of approaches by improving a single module.

Several example methods are included, which provide local explanations (explaining the prediction of a single instance, e.g. LIME and SHAP) or global explanations (explaining the dataset, or model behavior on the dataset, e.g. TokenFrequency and MMDCritic). By replacing the default modules (e.g. local data generation, global data sampling or improved embedding methods), these methods can be improved upon or new methods can be introduced.

© Marcel Robeer, 2021

Quick tour

Local explanation: explain a models' prediction on a given sample, self-provided or from a dataset.

from text_explainability import LIME, LocalTree

# Define sample to explain
sample = 'Explain why this is positive and not negative!'

# LIME explanation (local feature importance)
LIME().explain(sample, model).scores

# List of local rules, extracted from tree
LocalTree().explain(sample, model).rules

Global explanation: explain the whole dataset (e.g. train set, test set), and what they look like for the ground-truth or predicted labels.

from text_explainability import import_data, TokenFrequency, MMDCritic

# Import dataset
env = import_data('./datasets/test.csv', data_cols=['fulltext'], label_cols=['label'])

# Top-k most frequent tokens per label
TokenFrequency(env.dataset).explain(labelprovider=env.labels, explain_model=False, k=3)

# 2 prototypes and 1 criticisms for the dataset
MMDCritic(env.dataset)(n_prototypes=2, n_criticisms=1)

Installation

See the installation instructions for an extended installation guide.

Method	Instructions
pip	Install from PyPI via pip3 install text_explainability. To speed up the explanation generation process use pip3 install text_explainability[fast].
Local	Clone this repository and install via pip3 install -e . or locally run python3 setup.py install.

Documentation

Full documentation of the latest version is provided at https://text-explainability.readthedocs.io/.

Example usage

See example usage 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]
	LocalRules	Fit a local sparse set of label-specific rules using SkopeRules.	github/skope-rules
	FoilTree	Fit a local contrastive/counterfactual decision tree around a single decision.	[Robeer2018]
	BayLIME	Bayesian extension of LIME for include prior knowledge and more consistent explanations.	[Zhao201]
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.

Extensions

text_explainability can be extended to also perform sensitivity testing, checking for machine learning model robustness and fairness. The text_sensitivity package is available through PyPI and fully documented at https://text-sensitivity.rtfd.io/.

Citation

@misc{text_explainability,
  title = {Python package text\_explainability},
  author = {Marcel Robeer},
  howpublished = {\url{https://git.science.uu.nl/m.j.robeer/text_explainability}},
  year = {2021}
}

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

Credits

• Florian Gardin, Ronan Gautier, Nicolas Goix, Bibi Ndiaye and Jean-Matthieu Schertzer. Skope-rules. 2020.
• Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini and Fosca Gianotti. Local Rule-Based Explanations of Black Box Decision Systems. 2018.
• Been Kim, Rajiv Khanna and Oluwasanmi O. Koyejo. Examples are not Enough, Learn to Criticize! Criticism for Interpretability. Advances in Neural Information Processing Systems (NIPS 2016). 2016.
• Scott Lundberg and Su-In Lee. A Unified Approach to Interpreting Model Predictions. 31st Conference on Neural Information Processing Systems (NIPS 2017). 2017.
• Christoph Molnar. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2021.
• Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2016). 2016.
• Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin. Anchors: High-Precision Model-Agnostic Explanations. AAAI Conference on Artificial Intelligence (AAAI). 2018.
• Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis and Mark Neerincx. "Contrastive Explanations with Local Foil Trees". 2018 Workshop on Human Interpretability in Machine Learning (WHI 2018). 2018.