fairlearn.postprocessing package

This module contains methods which operate on a predictor, rather than an estimator.

The predictor’s output is adjusted to fulfill specified parity constraints. The postprocessors learn how to adjust the predictor’s output from the training data.

class fairlearn.postprocessing.ThresholdOptimizer(*, estimator=None, constraints='demographic_parity', objective='accuracy_score', grid_size=1000, flip=False, prefit=False)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.MetaEstimatorMixin

A classifier based on the threshold optimization approach.

The classifier is obtained by applying group-specific thresholds to the provided estimator. The thresholds are chosen to optimize the provided performance objective subject to the provided fairness constraints.

Parameters
  • estimator (estimator object implementing 'predict' and possibly 'fit') – An estimator whose output is postprocessed.

  • constraints (str, default='demographic_parity') –

    Fairness constraints under which threshold optimization is performed. Possible inputs are:

    ’demographic_parity’, ‘selection_rate_parity’ (synonymous)

    match the selection rate across groups

    ’{false,true}_{positive,negative}_rate_parity’

    match the named metric across groups

    ’equalized_odds’

    match true positive and false positive rates across groups

  • objective (str, default='accuracy_score') –

    Performance objective under which threshold optimization is performed. Not all objectives are allowed for all types of constraints. Possible inputs are:

    ’accuracy_score’, ‘balanced_accuracy_score’

    allowed for all constraint types

    ’selection_rate’, ‘true_positive_rate’, ‘true_negative_rate’,

    allowed for all constraint types except ‘equalized_odds’

  • grid_size (int, default=1000) – The values of the constraint metric are discretized according to the grid of the specified size over the interval [0,1] and the optimization is performed with respect to the constraints achieving those values. In case of ‘equalized_odds’ the constraint metric is the false positive rate.

  • flip (bool, default=False) – If True, then allow flipping the decision if it improves the resulting

  • prefit (bool, default=False) – If True, avoid refitting the given estimator. Note that when used with sklearn.model_selection.cross_val_score(), sklearn.model_selection.GridSearchCV, this will result in an error. In that case, please use prefit=False.

Notes

The procedure is based on the algorithm of Hardt et al. (2016).

fit(X, y, *, sensitive_features, **kwargs)[source]

Fit the model.

The fit is based on training features and labels, sensitive features, as well as the fairness-unaware predictor or estimator. If an estimator was passed in the constructor this fit method will call fit(X, y, **kwargs) on said estimator.

Parameters
predict(X, *, sensitive_features, random_state=None)[source]

Predict label for each sample in X while taking into account sensitive features.

Parameters
Returns

The prediction. If X represents the data for a single example the result will be a scalar. Otherwise the result will be a vector

Return type

Scalar or vector as numpy.ndarray

fairlearn.postprocessing.plot_threshold_optimizer(threshold_optimizer, ax=None, show_plot=True)[source]

Plot the chosen solution of the threshold optimizer.

For fairlearn.postprocessing.ThresholdOptimizer objects that have their constraint set to ‘demographic_parity’ this will result in a selection/error curve plot. For fairlearn.postprocessing.ThresholdOptimizer objects that have their constraint set to ‘equalized_odds’ this will result in a ROC curve plot.

Parameters
  • threshold_optimizer (fairlearn.postprocessing.ThresholdOptimizer) – the ThresholdOptimizer instance for which the results should be illustrated.

  • ax (matplotlib.axes.Axes) – a custom matplotlib.axes.Axes object to use for the plots, default None

  • show_plot (bool) – whether or not the generated plot should be shown, default True