semvec/pypi-packages huggingface dataset:
equal-odds 0.0.7
_PACKAGE UNDER CONSTRUCTION_
License: MIT
Requires Python: >=3.8
Keywords: ml,optimization,fairness

# equal-odds

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Fast postprocessing of any score-based predictor to meet fairness criteria.

The `equal-odds` package can achieve strict or relaxed fairness constraint fulfillment, 
which can be useful to compare ML models at equal fairness levels.


## Installing

Install package from [PyPI](https://pypi.org/project/equal-odds/):
```
pip install equal-odds
```

Or, for development, you can clone the repo and install from local sources:
```
git clone https://github.com/AndreFCruz/equal-odds.git
pip install ./equal-odds
```


## Getting started

```py
# Given any trained model that outputs real-valued scores
fair_clf = RelaxedEqualOdds(
    predictor=lambda X: model.predict_proba(X)[:, -1],   # for sklearn API
    # predictor=model,  # use this for a callable model
    tolerance=0.05,     # fairness constraint tolerance
)

# Fit the fairness adjustment on some data
# This will find the optimal _fair classifier_
fair_clf.fit(X=X, y=y, group=group)

# Now you can use `fair_clf` as any other classifier
# You have to provide group information to compute fair predictions
y_pred_test = fair_clf(X=X_test, group=group_test)
```


## How it works

Given a callable score-based predictor (i.e., `y_pred = predictor(X)`), and some `(X, Y, S)` data to fit, `RelaxedEqualOdds` will:
1. Compute group-specific ROC curves and their convex hulls;
2. Compute the `r`-relaxed optimal solution for the chosen fairness criterion (using [cvxpy](https://www.cvxpy.org));
3. Find the set of group-specific binary classifiers that match the optimal solution found.
    - each group-specific classifier is made up of (possibly randomized) group-specific thresholds over the given predictor;
    - if a group's ROC point is in the interior of its ROC curve, partial randomization of its predictions may be necessary.


## Implementation road-map

We welcome community contributions for [cvxpy](https://www.cvxpy.org) implementations of other fairness constraints.

Currently implemented fairness constraints:
- [x] equality of odds [(Hardt et al., 2016)](https://proceedings.neurips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf);
  - i.e., equal group-specific TPR and FPR;
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- [ ] equal opportunity;
  - i.e., equal group-specific TPR;
- [ ] demographic parity;
  - i.e., equal group-specific predicted prevalence;
-->