fairlearn.metrics.equalized_odds_ratio

fairlearn.metrics.equalized_odds_ratio(y_true, y_pred, *, sensitive_features, method='between_groups', sample_weight=None, agg='worst_case')

Calculate the equalized odds ratio.

The smaller of two metrics: true_positive_rate_ratio and false_positive_rate_ratio. The former is the ratio between the smallest and largest of \(P[h(X)=1 | A=a, Y=1]\), across all values \(a\) of the sensitive feature(s). The latter is defined similarly, but for \(P[h(X)=1 | A=a, Y=0]\). The equalized odds ratio of 1 means that all groups have the same true positive, true negative, false positive, and false negative rates.

Read more in the User Guide.

Return type:

float

Parameters:

y_truearray-like

Ground truth (correct) labels.

y_predarray-like

Predicted labels \(h(X)\) returned by the classifier.

sensitive_featuresarray-like

The sensitive features over which equalized odds should be assessed

methodstring {‘between_groups’, ‘to_overall’}, default between_groups

How to compute the differences. See fairlearn.metrics.MetricFrame.ratio() for details.

sample_weightarray-like

The sample weights

aggstring {‘worst_case’, ‘mean’}, default worst_case

The aggregation method. One of “worst_case” or “mean”. If “worst_case”, the smaller one of the false positive rate ratio and true positive rate ratio is returned. If “mean”, the mean of the ratios is returned.

Returns:

float

The equalized odds ratio