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

Code before:
    raise ValueError("Reduction parameter unknown.")


def class_reduce(
    num: torch.Tensor, denom: torch.Tensor, weights: torch.Tensor, class_reduction: str = "none"
) -> torch.Tensor:
    """
    Function used to reduce classification metrics of the form `num / denom * weights`.
    For example for calculating standard accuracy the num would be number of
    true positives per class, denom would be the support per class, and weights
    would be a tensor of 1s

    Args:
        num: numerator tensor
        denom: denominator tensor
        weights: weights for each class
        class_reduction: reduction method for multiclass problems

            - ``'micro'``: calculate metrics globally (default)
            - ``'macro'``: calculate metrics for each label, and find their unweighted mean.
            - ``'weighted'``: calculate metrics for each label, and find their weighted mean.
            - ``'none'`` or ``None``: returns calculated metric per class

    Raises:
        ValueError:
            If ``class_reduction`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"`` or ``None``
    """
    valid_reduction = ("micro", "macro", "weighted", "none", None)
    if class_reduction == "micro":
        fraction = torch.sum(num) / torch.sum(denom)
    else:
        fraction = num / denom

    # We need to take care of instances where the denom can be 0
    # for some (or all) classes which will produce nans
    fraction[fraction != fraction] = 0

    if class_reduction == "micro":
        return fraction
    elif class_reduction == "macro":
        return torch.mean(fraction)
    elif class_reduction == "weighted":
        return torch.sum(fraction * (weights.float() / torch.sum(weights)))
    elif class_reduction == "none" or class_reduction is None:
        return fraction

    raise ValueError(
        f"Reduction parameter {class_reduction} unknown."
        f" Choose between one of these: {valid_reduction}"
    )


Review comment (suggestion):
```suggestion
            If ``class_reduction`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"`` or ``None``.
```

Code after:
    raise ValueError("Reduction parameter unknown.")


def class_reduce(
    num: torch.Tensor, denom: torch.Tensor, weights: torch.Tensor, class_reduction: str = "none"
) -> torch.Tensor:
    """
    Function used to reduce classification metrics of the form `num / denom * weights`.
    For example for calculating standard accuracy the num would be number of
    true positives per class, denom would be the support per class, and weights
    would be a tensor of 1s

    Args:
        num: numerator tensor
        denom: denominator tensor
        weights: weights for each class
        class_reduction: reduction method for multiclass problems

            - ``'micro'``: calculate metrics globally (default)
            - ``'macro'``: calculate metrics for each label, and find their unweighted mean.
            - ``'weighted'``: calculate metrics for each label, and find their weighted mean.
            - ``'none'`` or ``None``: returns calculated metric per class

    Raises:
        ValueError:
            If ``class_reduction`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"`` or ``None``.
    """
    valid_reduction = ("micro", "macro", "weighted", "none", None)
    if class_reduction == "micro":
        fraction = torch.sum(num) / torch.sum(denom)
    else:
        fraction = num / denom

    # We need to take care of instances where the denom can be 0
    # for some (or all) classes which will produce nans
    fraction[fraction != fraction] = 0

    if class_reduction == "micro":
        return fraction
    elif class_reduction == "macro":
        return torch.mean(fraction)
    elif class_reduction == "weighted":
        return torch.sum(fraction * (weights.float() / torch.sum(weights)))
    elif class_reduction == "none" or class_reduction is None:
        return fraction

    raise ValueError(
        f"Reduction parameter {class_reduction} unknown."
        f" Choose between one of these: {valid_reduction}"
    )