# Buggy code
def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
    """Compute Area Under the Curve (AUC) from prediction scores

    Note: this implementation is restricted to the binary classification task
    or multilabel classification task in label indicator format.

    Parameters
    ----------
    y_true : array, shape = [n_samples] or [n_samples, n_classes]
        True binary labels in binary label indicators.

    y_score : array, shape = [n_samples] or [n_samples, n_classes]
        Target scores, can either be probability estimates of the positive
        class, confidence values, or binary decisions.

    average : string, [None, 'micro', 'macro' (default), 'samples', 'weighted']
        If ``None``, the scores for each class are returned. Otherwise,
        this determines the type of averaging performed on the data:

        ``'micro'``:
            Calculate metrics globally by considering each element of the label
            indicator matrix as a label.
        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean.  This does not take label imbalance into account.
        ``'weighted'``:
            Calculate metrics for each label, and find their average, weighted
            by support (the number of true instances for each label).
        ``'samples'``:
            Calculate metrics for each instance, and find their average.

    sample_weight : array-like of shape = [n_samples], optional
        Sample weights.

    Returns
    -------
    auc : float

    References
    ----------
    .. [1] `Wikipedia entry for the Receiver operating characteristic
            <http://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_

    See also
    --------
    average_precision_score : Area under the precision-recall curve

    roc_curve : Compute Receiver operating characteristic (ROC)

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import roc_auc_score
    >>> y_true = np.array([0, 0, 1, 1])
    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> roc_auc_score(y_true, y_scores)
    0.75

    """

    def _binary_roc_auc_score(y_true, y_score, sample_weight=None):
        if len(np.unique(y_true)) != 2:
            raise ValueError("ROC AUC score is not defined")

        fpr, tpr, tresholds = roc_curve(y_true, y_score, sample_weight=sample_weight)
        return auc(fpr, tpr, reorder=True)

    return _average_binary_score(
        _binary_roc_auc_score, y_true, y_score, average, sample_weight=sample_weight
    )


# Traceback (IndexError)
======================================================================
ERROR: sklearn.tests.test_common.test_regressors_train('RANSACRegressor', <class 'sklearn.linear_model.ransac.RANSACRegr
essor'>, array([[-0.44836249, -0.47282444, -1.20608008, ..., -0.75500806,
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
self.test(*self.arg)
File "C:\Python27\lib\site-packages\sklearn\tests\test_common.py", line 850, in check_regressors_train
assert_raises(ValueError, regressor.fit, X, y[:-1])
File "C:\Python27\lib\unittest\case.py", line 473, in assertRaises
callableObj(*args, **kwargs)
File "C:\Python27\lib\site-packages\sklearn\linear_model\ransac.py", line 261, in fit
y_subset = y[subset_idxs]
IndexError: index 199 is out of bounds for axis 0 with size 199

# Fixed code
def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
    """Compute Area Under the Curve (AUC) from prediction scores

    Note: this implementation is restricted to the binary classification task
    or multilabel classification task in label indicator format.

    Parameters
    ----------
    y_true : array, shape = [n_samples] or [n_samples, n_classes]
        True binary labels in binary label indicators.

    y_score : array, shape = [n_samples] or [n_samples, n_classes]
        Target scores, can either be probability estimates of the positive
        class, confidence values, or binary decisions.

    average : string, [None, 'micro', 'macro' (default), 'samples', 'weighted']
        If ``None``, the scores for each class are returned. Otherwise,
        this determines the type of averaging performed on the data:

        ``'micro'``:
            Calculate metrics globally by considering each element of the label
            indicator matrix as a label.
        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean.  This does not take label imbalance into account.
        ``'weighted'``:
            Calculate metrics for each label, and find their average, weighted
            by support (the number of true instances for each label).
        ``'samples'``:
            Calculate metrics for each instance, and find their average.

    sample_weight : array-like of shape = [n_samples], optional
        Sample weights.

    Returns
    -------
    auc : float

    References
    ----------
    .. [1] `Wikipedia entry for the Receiver operating characteristic
            <http://en.wikipedia.org/wiki/Receiver_operating_characteristic>`_

    See also
    --------
    average_precision_score : Area under the precision-recall curve

    roc_curve : Compute Receiver operating characteristic (ROC)

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.metrics import roc_auc_score
    >>> y_true = np.array([0, 0, 1, 1])
    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
    >>> roc_auc_score(y_true, y_scores)
    0.75

    """

    def _binary_roc_auc_score(y_true, y_score, sample_weight=None):
        if len(np.unique(y_true)) != 2:
            raise ValueError(
                "Only one class present in y_true. ROC AUC score "
                "is not defined in that case."
            )

        fpr, tpr, tresholds = roc_curve(y_true, y_score, sample_weight=sample_weight)
        return auc(fpr, tpr, reorder=True)

    return _average_binary_score(
        _binary_roc_auc_score, y_true, y_score, average, sample_weight=sample_weight
    )