(
    favorable_labels, protected_attributes, unfavorable_labels=None, fairness_weight=0.5
)
return _AccuracyAndDisparateImpact(
        favorable_labels, protected_attributes, unfavorable_labels, fairness_weight
    )
Create a scikit-learn compatible combined scorer for `accuracy`_
    and `symmetric disparate impact`_ given the fairness info.
    The scorer is suitable for classification problems,
    with higher resulting scores indicating better outcomes.
    The result is a linear combination of accuracy and
    symmetric disparate impact, and is between 0 and 1.
    This metric can be used as the `scoring` argument
    of an optimizer such as `Hyperopt`_, as shown in this `demo`_.

    .. _`accuracy`: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
    .. _`symmetric disparate impact`: lale.lib.aif360.util.html#lale.lib.aif360.util.symmetric_disparate_impact
    .. _`Hyperopt`: lale.lib.lale.hyperopt.html#lale.lib.lale.hyperopt.Hyperopt
    .. _`demo`: https://nbviewer.jupyter.org/github/IBM/lale/blob/master/examples/demo_aif360.ipynb
Create a scikit-learn compatible combined scorer for `accuracy`_
    and `symmetric disparate impact`_ given the fairness info.
    The scorer is suitable for classification problems,
    with higher resulting scores indicating better outcomes.
    The result is a linear combination of accuracy and
    symmetric disparate impact, and is between 0 and 1.
    This metric can be used as the `scoring` argument
    of an optimizer such as `Hyperopt`_, as shown in this `demo`_.
Create a scikit - learn compatible combined scorer for accuracy _ and symmetric disparate impact _ given the fairness info . The scorer is suitable for classification problems with higher resulting scores indicating better outcomes . The result is a linear combination of accuracy and symmetric disparate impact and is between 0 and 1 . This metric can be used as the scoring argument of an optimizer such as Hyperopt _ as shown in this demo _ .
def accuracy_and_disparate_impact(
    favorable_labels, protected_attributes, unfavorable_labels=None, fairness_weight=0.5
):
    """
    Create a scikit-learn compatible combined scorer for `accuracy`_
    and `symmetric disparate impact`_ given the fairness info.
    The scorer is suitable for classification problems,
    with higher resulting scores indicating better outcomes.
    The result is a linear combination of accuracy and
    symmetric disparate impact, and is between 0 and 1.
    This metric can be used as the `scoring` argument
    of an optimizer such as `Hyperopt`_, as shown in this `demo`_.

    .. _`accuracy`: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
    .. _`symmetric disparate impact`: lale.lib.aif360.util.html#lale.lib.aif360.util.symmetric_disparate_impact
    .. _`Hyperopt`: lale.lib.lale.hyperopt.html#lale.lib.lale.hyperopt.Hyperopt
    .. _`demo`: https://nbviewer.jupyter.org/github/IBM/lale/blob/master/examples/demo_aif360.ipynb"""
    return _AccuracyAndDisparateImpact(
        favorable_labels, protected_attributes, unfavorable_labels, fairness_weight
    )
def accuracy_and_disparate_impact ( favorable_labels , protected_attributes , unfavorable_labels = None , fairness_weight = 0.5 ) : return _AccuracyAndDisparateImpact ( favorable_labels , protected_attributes , unfavorable_labels , fairness_weight )