def roc_auc_score(gold, probs, ignore_in_gold=[], ignore_in_pred=[]):
    """Compute the ROC AUC score, given the gold labels and predicted probs.

    Args:
        gold: A 1d array-like of gold labels
        probs: A 2d array-like of predicted probabilities
        ignore_in_gold: A list of labels for which elements having that gold
            label will be ignored.

    Returns:
        roc_auc_score: The (float) roc_auc score
    """
    gold = arraylike_to_numpy(gold)

    # Filter out the ignore_in_gold (but not ignore_in_pred)
    # Note the current sub-functions (below) do not handle this...
    if len(ignore_in_pred) > 0:
        raise ValueError("ignore_in_pred not defined for ROC-AUC score.")
    keep = [x not in ignore_in_gold for x in gold]
    gold = gold[keep]
    probs = probs[keep, :]

    # Convert gold to one-hot indicator format, using the k inferred from probs
    gold_s = pred_to_prob(torch.from_numpy(gold), k=probs.shape[1]).numpy()
    return skm.roc_auc_score(gold_s, probs)
Compute the ROC AUC score, given the gold labels and predicted probs.

    Args:
        gold: A 1d array-like of gold labels
        probs: A 2d array-like of predicted probabilities
        ignore_in_gold: A list of labels for which elements having that gold
            label will be ignored.

    Returns:
        roc_auc_score: The (float) roc_auc score