(
    favorable_labels, protected_attributes, unfavorable_labels=None
)
return _SymmetricDisparateImpact(
        favorable_labels, protected_attributes, unfavorable_labels
    )
Create a scikit-learn compatible scorer for symmetric `disparate impact`_ given the fairness info.
    For disparate impact <= 1.0, return that value, otherwise return
    its inverse.  The result is between 0 and 1.  The higher this
    metric, the better, and the ideal value is 1.  A value <1 implies
    that either the privileged group or the unprivileged group is
    receiving a disparate benefit.

    .. _`disparate impact`: lale.lib.aif360.util.html#lale.lib.aif360.util.disparate_impact
Create a scikit-learn compatible scorer for symmetric `disparate impact`_ given the fairness info.
    For disparate impact <= 1.0, return that value, otherwise return
    its inverse.  The result is between 0 and 1.  The higher this
    metric, the better, and the ideal value is 1.  A value <1 implies
    that either the privileged group or the unprivileged group is
    receiving a disparate benefit.
Create a scikit - learn compatible scorer for symmetric disparate impact _ given the fairness info . For disparate impact < = 1 . 0 return that value otherwise return its inverse . The result is between 0 and 1 . The higher this metric the better and the ideal value is 1 . A value <1 implies that either the privileged group or the unprivileged group is receiving a disparate benefit .
def symmetric_disparate_impact(
    favorable_labels, protected_attributes, unfavorable_labels=None
):
    """
    Create a scikit-learn compatible scorer for symmetric `disparate impact`_ given the fairness info.
    For disparate impact <= 1.0, return that value, otherwise return
    its inverse.  The result is between 0 and 1.  The higher this
    metric, the better, and the ideal value is 1.  A value <1 implies
    that either the privileged group or the unprivileged group is
    receiving a disparate benefit.

    .. _`disparate impact`: lale.lib.aif360.util.html#lale.lib.aif360.util.disparate_impact"""
    return _SymmetricDisparateImpact(
        favorable_labels, protected_attributes, unfavorable_labels
    )
def symmetric_disparate_impact ( favorable_labels , protected_attributes , unfavorable_labels = None ) : return _SymmetricDisparateImpact ( favorable_labels , protected_attributes , unfavorable_labels )