Repository: Lightning-AI/pytorch-lightning
File: pytorch_lightning/metrics/functional/classification.py (Python)

Code before:
    Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from multiclass
    prediction scores

    Args:
        pred: estimated probabilities, with shape [N, C]
        target: ground-truth labels, with shape [N,]
        sample_weight: sample weights
        num_classes: number of classes (default: None, computes automatically from data)

    Return:
        Tensor containing ROCAUC score

    Example:

        >>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
        ...                      [0.05, 0.85, 0.05, 0.05],
        ...                      [0.05, 0.05, 0.85, 0.05],
        ...                      [0.05, 0.05, 0.05, 0.85]])
        >>> target = torch.tensor([0, 1, 3, 2])
        >>> multiclass_auroc(pred, target)   # doctest: +NORMALIZE_WHITESPACE
        tensor(0.6667)
    """
    if not torch.allclose(pred.sum(dim=1), torch.tensor(1.0)):
        raise ValueError(
            "Multiclass AUROC metric expects the target scores to be "
            "probabilities, i.e. they should sum up to 1.0 over classes")

    if torch.unique(target).size(0) != pred.size(1):
        raise ValueError(
            f"Number of classes found in in 'target' ({torch.unique(target).size(0)}) "
            f"does not equal the number of columns in 'pred' ({pred.size(1)}). "
            "Multiclass AUROC is not defined when all of the classes do not "
            "occur in the target labels.")

    if num_classes is not None and num_classes != pred.size(1):
        raise ValueError(
            f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal "
            f"the number of classes passed in 'num_classes' ({num_classes}).")

    @multiclass_auc_decorator(reorder=False)
    def _multiclass_auroc(pred, target, sample_weight, num_classes):
        return multiclass_roc(pred, target, sample_weight, num_classes)

    class_aurocs = _multiclass_auroc(pred=pred, target=target,
                                     sample_weight=sample_weight,
                                     num_classes=num_classes)
    return torch.mean(class_aurocs)


def average_precision(
        pred: torch.Tensor,

Review comment (suggestion):
```suggestion
        raise ValueError(
            "Multiclass AUROC metric expects the target scores to be"
            " probabilities, i.e. they should sum up to 1.0 over classes")
```

Code after:

    if num_classes is not None and num_classes != pred.size(1):
        raise ValueError(
            f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal"
            f" the number of classes passed in 'num_classes' ({num_classes}).")

    @multiclass_auc_decorator(reorder=False)
    def _multiclass_auroc(pred, target, sample_weight, num_classes):
        return multiclass_roc(pred, target, sample_weight, num_classes)

    class_aurocs = _multiclass_auroc(pred=pred, target=target,
                                     sample_weight=sample_weight,
                                     num_classes=num_classes)
    return torch.mean(class_aurocs)


def average_precision(
        pred: torch.Tensor,
        target: torch.Tensor,
        sample_weight: Optional[Sequence] = None,
        pos_label: int = 1.,
) -> torch.Tensor:
    """
    Compute average precision from prediction scores

    Args:
        pred: estimated probabilities
        target: ground-truth labels
        sample_weight: sample weights
        pos_label: the label for the positive class

    Return:
        Tensor containing average precision score

    Example:

        >>> x = torch.tensor([0, 1, 2, 3])
        >>> y = torch.tensor([0, 1, 2, 2])
        >>> average_precision(x, y)
        tensor(0.3333)
    """
    precision, recall, _ = precision_recall_curve(pred=pred, target=target,
                                                  sample_weight=sample_weight,
                                                  pos_label=pos_label)
    # Return the step function integral
    # The following works because the last entry of precision is
    # guaranteed to be 1, as returned by precision_recall_curve
    return -torch.sum((recall[1:] - recall[:-1]) * precision[:-1])


def dice_score(