src/aif360/sklearn/README.md

aif360.sklearn

This is a wholly separate interface for interacting with data, viewing metrics, and running debiasing algorithms than the main AIF360 package. The purpose of this sub-package is to match scikit-learn paradigms/APIs for easier integration in typical machine learning workflows.

See Getting Started to see aif360.sklearn in action.

To do:

• Reformat datasets as separate X and y (and sample_weight) DataFrame objects with sample properties (protected attributes) as the index
• Load included datasets in the above format
  • Use sklearn.datasets.fetch_openml to load UCI datasets (#53)
  • COMPAS
  • MEPS
• Implement metrics as individual functions instead of instance methods
  • Make certain metrics compatible as sklearn scorers
  • Use "prot_attr" and "priv_group" keywords to specify protected attributes to functions
  • Generalized confusion matrix
  • Sample distortion metrics
• Make inprocessing algorithms compatible as sklearn Estimators
  • Adversarial debiasing
  • [External] get_feature_names() from data preprocessing steps that would remove DataFrame formatting
    • SLEP007/SLEP008 - feature names
  • Prejudice remover
  • Meta-fair classifier
• Make preprocessing algorithms compatible as sklearn Transformers
  • [External] Add functionality to modify X and y
    • SLEP005 - Resampler API (see discussion; meta-estimator workaround may be enough)
  • Disparate impact remover
  • Learning fair representations
  • Optimized preprocessing
  • Reweighing
    • Meta-estimator workaround
    • [External] SLEP006 - Sample properties (meta-estimator works but would be very nice to have)
• Make postprocessing algorithms compatible
  • Calibrated equalized odds postprocessing
    • Meta-estimator workaround again
  • Equalized odds postprocessing
  • Reject option classification
• Miscellaneous:
  • Explainers