How to use the fairlearn.postprocessing._constants.SCORE_KEY function in fairlearn

def test_calculate_roc_points():
    data = pd.DataFrame({
        SENSITIVE_FEATURE_KEY: sensitive_features_ex1,
        SCORE_KEY: scores_ex,
        LABEL_KEY: labels_ex})
    grouped_data = data.groupby(SENSITIVE_FEATURE_KEY).get_group("A") \
        .sort_values(by=SCORE_KEY, ascending=False)

    roc_points = _calculate_roc_points(grouped_data, "A")
    expected_roc_points = pd.DataFrame({
        "x": [0, 0.25, 0.5, 0.5, 1],
        "y": [0, 1/3,  2/3, 1,   1],
        "operation": [ThresholdOperation('>', np.inf),
                      ThresholdOperation('<', 0.5),
                      ThresholdOperation('<', 1.5),
                      ThresholdOperation('<', 2.5),
                      ThresholdOperation('>', -np.inf)]
    })

    _assert_equal_points(expected_roc_points, roc_points)

def test_calculate_roc_points():
    data = pd.DataFrame({
        SENSITIVE_FEATURE_KEY: sensitive_features_ex1,
        SCORE_KEY: scores_ex,
        LABEL_KEY: labels_ex})
    grouped_data = data.groupby(SENSITIVE_FEATURE_KEY).get_group("A") \
        .sort_values(by=SCORE_KEY, ascending=False)

    roc_points = _calculate_roc_points(grouped_data, "A")
    expected_roc_points = pd.DataFrame({
        "x": [0, 0.25, 0.5, 0.5, 1],
        "y": [0, 1/3,  2/3, 1,   1],
        "operation": [ThresholdOperation('>', np.inf),
                      ThresholdOperation('<', 0.5),
                      ThresholdOperation('<', 1.5),
                      ThresholdOperation('<', 2.5),
                      ThresholdOperation('>', -np.inf)]
    })

    _assert_equal_points(expected_roc_points, roc_points)

    # Try filtering to get the convex hull of the ROC points.
    # This should drop the second and third point.

def _get_grouped_data_and_base_points(sensitive_feature_value):
    data = pd.DataFrame({
        SENSITIVE_FEATURE_KEY: sensitive_features_ex1,
        SCORE_KEY: scores_ex,
        LABEL_KEY: labels_ex})
    grouped_data = data.groupby(SENSITIVE_FEATURE_KEY).get_group(sensitive_feature_value) \
        .sort_values(by=SCORE_KEY, ascending=False)
    x_grid = np.linspace(0, 1, 100)

    if sensitive_feature_value == "A":
        expected_roc_points = pd.DataFrame({
            "x": [0, 0.25, 0.5, 0.5, 1],
            "y": [0, 1/3,  2/3, 1,   1],
            "operation": [ThresholdOperation('>', np.inf),
                          ThresholdOperation('<', 0.5),
                          ThresholdOperation('<', 1.5),
                          ThresholdOperation('<', 2.5),
                          ThresholdOperation('>', -np.inf)]
        })
        ignore_for_base_points = [1, 2]

def _get_grouped_data_and_base_points(sensitive_feature_value):
    data = pd.DataFrame({
        SENSITIVE_FEATURE_KEY: sensitive_features_ex1,
        SCORE_KEY: scores_ex,
        LABEL_KEY: labels_ex})
    grouped_data = data.groupby(SENSITIVE_FEATURE_KEY).get_group(sensitive_feature_value) \
        .sort_values(by=SCORE_KEY, ascending=False)
    x_grid = np.linspace(0, 1, 100)

    if sensitive_feature_value == "A":
        expected_roc_points = pd.DataFrame({
            "x": [0, 0.25, 0.5, 0.5, 1],
            "y": [0, 1/3,  2/3, 1,   1],
            "operation": [ThresholdOperation('>', np.inf),
                          ThresholdOperation('<', 0.5),
                          ThresholdOperation('<', 1.5),
                          ThresholdOperation('<', 2.5),
                          ThresholdOperation('>', -np.inf)]
        })
        ignore_for_base_points = [1, 2]

    if sensitive_feature_value == "B":
        expected_roc_points = pd.DataFrame({

def _get_scores_labels_and_counts(data):
    """Order samples by scores, counting number of positive, negative, and overall samples.

    The samples are sorted into ascending order.

    :param data: the DataFrame containing scores and labels
    :type data: pandas.DataFrame
    :return: a tuple containing the sorted scores, labels, the number of samples, the number
        of positive samples, and the number of negative samples
    :rtype: tuple of list, list, int, int, int
    """
    data_sorted = data.sort_values(by=SCORE_KEY, ascending=False)

    scores = list(data_sorted[SCORE_KEY])
    labels = list(data_sorted[LABEL_KEY])

    n, n_positive, n_negative = _get_counts(labels)

    return scores, labels, n, n_positive, n_negative

:return: the training data for the mitigator, grouped by sensitive feature value
    :rtype: pandas.DataFrameGroupBy
    """
    data_dict = {}

    # TODO: extend to multiple columns for additional group data
    # and name columns after original column names if possible
    # or store the original column names
    sensitive_feature_name = SENSITIVE_FEATURE_KEY
    if sensitive_feature_names is not None:
        if sensitive_feature_name in [SCORE_KEY, LABEL_KEY]:
            raise ValueError(SENSITIVE_FEATURE_NAME_CONFLICT_DETECTED_ERROR_MESSAGE)
        sensitive_feature_name = sensitive_feature_names[0]

    _reformat_data_into_dict(sensitive_feature_name, data_dict, sensitive_features)
    _reformat_data_into_dict(SCORE_KEY, data_dict, scores)
    _reformat_data_into_dict(LABEL_KEY, data_dict, labels)

    return pd.DataFrame(data_dict).groupby(sensitive_feature_name)

def _get_scores_labels_and_counts(data):
    """Order samples by scores, counting number of positive, negative, and overall samples.

    The samples are sorted into ascending order.

    :param data: the DataFrame containing scores and labels
    :type data: pandas.DataFrame
    :return: a tuple containing the sorted scores, labels, the number of samples, the number
        of positive samples, and the number of negative samples
    :rtype: tuple of list, list, int, int, int
    """
    data_sorted = data.sort_values(by=SCORE_KEY, ascending=False)

    scores = list(data_sorted[SCORE_KEY])
    labels = list(data_sorted[LABEL_KEY])

    n, n_positive, n_negative = _get_counts(labels)

    return scores, labels, n, n_positive, n_negative