text-sensitivity 0.2.0

Sensitivity testing (fairness & robustness) for text machine learning models

Extension of text_explainability

Uses the generic architecture of text_explainability to also include tests of robustness (how generalizable the model is in production, e.g. ability to handle input characters, stability when adding typos, or the effect of adding random unrelated data) and fairness (if equal individuals are treated equally by the model, e.g. subgroup fairness on sex and nationality).

© Marcel Robeer, 2021

Quick tour

Robustness: test whether your model is able to handle different data types...

from text_sensitivity import RandomAscii, RandomEmojis, combine_generators

# Generate 10 strings with random ASCII characters
RandomAscii().generate_list(n=10)

# Generate 5 strings with random ASCII characters and emojis
combine_generators(RandomAscii(), RandomEmojis()).generate_list(n=5)

... whether your model performs equally for different entities ...

from text_sensitivity import RandomAddress, RandomEmail

# Random address of your current locale (default = 'nl')
RandomAddress(sep=', ').generate_list(n=5)

# Random e-mail addresses in Spanish ('es') and Portuguese ('pt'), and include from which country the e-mail is
RandomEmail(languages=['es', 'pt']).generate_list(n=10, attributes=True)

... and if it is robust under simple perturbations.

from text_sensitivity import compare_accuracy
from text_sensitivity.perturbation import to_upper, add_typos

# Is model accuracy equal when we change all sentences to uppercase?
compare_accuracy(env, model, to_upper)

# Is model accracy equal when we add typos in words?
compare_accuracy(env, model, add_typos)

Fairness: see if performance is equal among subgroups.

from text_sensitivity import RandomName

# Generate random Dutch ('nl') and Russian ('ru') names, both 'male' and 'female' (+ return attributes)
RandomName(languages=['nl', 'ru'], sex=['male', 'female']).generate_list(n=10, attributes=True)

Installation

Documentation

Full documentation of the latest version is provided at https://marcelrobeer.github.io/text_sensitivity/.

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.

Releases

text_explainability is officially released through PyPI.

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

Citation

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

Maintenance

Contributors

• Marcel Robeer (@m.j.robeer)
• Elize Herrewijnen (@e.herrewijnen)

Todo

Tasks yet to be done:

• Word-level perturbations
• Add fairness-specific metrics:
  • Subgroup fairness
  • Counterfactual fairness
• Add expected behavior
  • Robustness: equal to prior prediction, or in some cases might expect that it deviates
  • Fairness: may deviate from original prediction
• Tests
  • Add tests for perturbations
  • Add tests for sensitivity testing schemes
• Add visualization ability

Credits

• Edward Ma. NLP Augmentation. 2019.
• Daniele Faraglia and other contributors. Faker. 2012.