(y_true, y_score, pos_label=None, sample_weight=None)
return _auc(fpr, tpr)
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
    from prediction scores.

    Parameters
    ----------
    y_true : array, shape = [n_samples] or [n_samples, n_classes]
        True binary _labels or 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 non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers). For binary
        y_true, y_score is supposed to be the score of the class with greater
        label.

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

    Returns
    -------
    auc : float
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
    from prediction scores.
Compute Area Under the Receiver Operating Characteristic Curve ( ROC AUC ) from prediction scores .
def roc_auc_score(y_true, y_score, pos_label=None, sample_weight=None):
    """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
    from prediction scores.

    Parameters
    ----------
    y_true : array, shape = [n_samples] or [n_samples, n_classes]
        True binary _labels or 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 non-thresholded measure of decisions
        (as returned by "decision_function" on some classifiers). For binary
        y_true, y_score is supposed to be the score of the class with greater
        label.

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

    Returns
    -------
    auc : float
    """
    fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, sample_weight=None)
    return _auc(fpr, tpr)
def roc_auc_score ( y_true , y_score , pos_label = None , sample_weight = None ) : fpr , tpr , _ = roc_curve ( y_true , y_score , pos_label = pos_label , sample_weight = None ) return _auc ( fpr , tpr )