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.

Added in version 0.4.6.

Parameters:

• y_true (array_like) – Ground truth (correct) labels.

• y_pred (array_like) – Predicted labels \(h(X)\) returned by the classifier.

• sensitive_features (array_like) – The sensitive features over which equalized odds should be assessed

• method (Literal['between_groups', 'to_overall']) – How to compute the differences. See fairlearn.metrics.MetricFrame.ratio() for details.

• sample_weight (array_like) – The sample weights

• agg (Literal['worst_case', 'mean']) – 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.

Return type:

float

Returns:

The equalized odds ratio

Raises:

ValueError – If agg is not one of 'worst_case' or 'mean'.