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## `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](examples/Getting%20Started.ipynb) to see `aif360.sklearn`
in action.
To do:
- [x] 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
- [x] Use `sklearn.datasets.fetch_openml` to load UCI datasets (#53)
- [ ] COMPAS
- [ ] MEPS
- [ ] Implement metrics as individual functions instead of instance methods
- [x] Make certain metrics compatible as sklearn scorers
- [x] Use "prot_attr" and "priv_group" keywords to specify protected attributes to
functions
- [x] Generalized confusion matrix
- [ ] Sample distortion metrics
- [ ] Make inprocessing algorithms compatible as sklearn `Estimator`s
- [x] Adversarial debiasing
- [ ] **[External]** `get_feature_names()` from data preprocessing
steps that would remove DataFrame formatting
- [ ] [SLEP007](https://github.com/scikit-learn/enhancement_proposals/pull/17)/[SLEP008](https://github.com/scikit-learn/enhancement_proposals/pull/18) - feature names
- [ ] Prejudice remover
- [ ] Meta-fair classifier
- [ ] Make preprocessing algorithms compatible as sklearn `Transformer`s
- [ ] **[External]** Add functionality to modify X and y
- [ ] [SLEP005](https://github.com/scikit-learn/enhancement_proposals/pull/15) - Resampler API (see discussion; meta-estimator workaround may be enough)
- [ ] Disparate impact remover
- [ ] Learning fair representations
- [ ] Optimized preprocessing
- [X] Reweighing
- [X] Meta-estimator workaround
- [ ] **[External]** [SLEP006](https://github.com/scikit-learn/enhancement_proposals/pull/16) - Sample properties (meta-estimator works but would be very nice to have)
- [ ] Make postprocessing algorithms compatible
- [x] Calibrated equalized odds postprocessing
- [x] Meta-estimator workaround again
- [ ] Equalized odds postprocessing
- [ ] Reject option classification
- [ ] Miscellaneous:
- [ ] Explainers
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