src/smclarify/bias/metrics/posttraining.py [204:225]:
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    feature: pd.Series,
    sensitive_facet_index: pd.Series,
    positive_label_index: pd.Series,
    positive_predicted_label_index: pd.Series,
) -> float:
    r"""
    Accuracy Difference (AD)

    :param feature: input feature
    :param sensitive_facet_index: boolean column indicating sensitive group
    :param positive_label_index: boolean column indicating positive labels
    :param positive_predicted_label_index: boolean column indicating positive predicted labels
    :return: Accuracy Difference between advantaged and disadvantaged classes
    """
    require(sensitive_facet_index.dtype == bool, "sensitive_facet_index must be of type bool")
    require(positive_label_index.dtype == bool, "positive_label_index must be of type bool")
    require(positive_predicted_label_index.dtype == bool, "positive_predicted_label_index must be of type bool")

    if len(feature[sensitive_facet_index]) == 0:
        raise ValueError("Facet set is empty")
    if len(feature[~sensitive_facet_index]) == 0:
        raise ValueError("Negated Facet set is empty")
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src/smclarify/bias/metrics/posttraining.py [281:303]:
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    feature: pd.Series,
    sensitive_facet_index: pd.Series,
    positive_label_index: pd.Series,
    positive_predicted_label_index: pd.Series,
) -> float:
    r"""
    Treatment Equality (TE)

    :param feature: input feature
    :param sensitive_facet_index: boolean column indicating sensitive group
    :param positive_label_index: boolean column indicating positive labels
    :param positive_predicted_label_index: boolean column indicating positive predicted labels
    :return: returns the difference in ratios between false negatives and false positives for the advantaged
        and disadvantaged classes
    """
    require(sensitive_facet_index.dtype == bool, "sensitive_facet_index must be of type bool")
    require(positive_label_index.dtype == bool, "positive_label_index must be of type bool")
    require(positive_predicted_label_index.dtype == bool, "positive_predicted_label_index must be of type bool")

    if len(feature[sensitive_facet_index]) == 0:
        raise ValueError("Facet set is empty")
    if len(feature[~sensitive_facet_index]) == 0:
        raise ValueError("Negated Facet set is empty")
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