def get_baseline_thresholds(
    scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN
) -> Tuple[float, List[float]]:
    """Returns baseline thresholds for multiclass/multilabel classification.

    Args:
        scores: estimated per-class scores/probabilities predicted by the model,
            numpy array with shape [num_examples, num_classes]
        labels: ground truth labels,
            numpy array with shape [num_examples, num_classes]
        objective: callable function, metric which we want to maximize

    Returns:
        tuple with best found objective score and per-class thresholds
    """
    num_classes = scores.shape[1]
    thresholds = [0.5] * num_classes
    predictions = np.greater(scores, thresholds).astype(np.int32)
    best_metric = objective(labels, predictions)
    return best_metric, thresholds
Returns baseline thresholds for multiclass/multilabel classification.

    Args:
        scores: estimated per-class scores/probabilities predicted by the model,
            numpy array with shape [num_examples, num_classes]
        labels: ground truth labels,
            numpy array with shape [num_examples, num_classes]
        objective: callable function, metric which we want to maximize

    Returns:
        tuple with best found objective score and per-class thresholds
    
catalyst/contrib/utils/thresholds.py