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

Code before:

        is_multiclass:
            Used only in certain special cases, where you want to treat inputs as a different type
            than what they appear to be. See the parameter's
            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
            for a more detailed explanation and examples.

        compute_on_step:
            Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()``
            before returning the value at the step
        process_group:
            Specify the process group on which synchronization is called.
            default: ``None`` (which selects the entire world)
        dist_sync_fn:
            Callback that performs the allgather operation on the metric state. When ``None``, DDP
            will be used to perform the allgather.

    Raises:
        ValueError:
            If ``threshold`` is not a ``float`` between ``0`` and ``1``
        ValueError:
            If ``reduce`` is none of ``"micro"``, ``"macro"`` or ``"samples"``
        ValueError:
            If ``mdmc_reduce`` is none of ``None``, ``"samplewise"``, ``"global"``
        ValueError:
            If ``reduce`` is set to ``"macro"`` and ``num_classes`` is not provided
        ValueError:
            If ``num_classes`` is set
            and ``ignore_index`` is not in the range ``0`` <= ``ignore_index`` < ``num_classes``

    Example:

        >>> from pytorch_lightning.metrics.classification import StatScores
        >>> preds  = torch.tensor([1, 0, 2, 1])
        >>> target = torch.tensor([1, 1, 2, 0])
        >>> stat_scores = StatScores(reduce='macro', num_classes=3)
        >>> stat_scores(preds, target)
        tensor([[0, 1, 2, 1, 1],
                [1, 1, 1, 1, 2],
                [1, 0, 3, 0, 1]])
        >>> stat_scores = StatScores(reduce='micro')
        >>> stat_scores(preds, target)
        tensor([2, 2, 6, 2, 4])

    """

    def __init__(
        self,
        threshold: float = 0.5,

Review comment (suggestion):
```suggestion
            If ``mdmc_reduce`` is none of ``None``, ``"samplewise"``, ``"global"``.
```

Code after:

        is_multiclass:
            Used only in certain special cases, where you want to treat inputs as a different type
            than what they appear to be. See the parameter's
            :ref:`documentation section <extensions/metrics:using the is_multiclass parameter>`
            for a more detailed explanation and examples.

        compute_on_step:
            Forward only calls ``update()`` and return ``None`` if this is set to ``False``.
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()``
            before returning the value at the step
        process_group:
            Specify the process group on which synchronization is called.
            default: ``None`` (which selects the entire world)
        dist_sync_fn:
            Callback that performs the allgather operation on the metric state. When ``None``, DDP
            will be used to perform the allgather.

    Raises:
        ValueError:
            If ``threshold`` is not a ``float`` between ``0`` and ``1``.
        ValueError:
            If ``reduce`` is none of ``"micro"``, ``"macro"`` or ``"samples"``.
        ValueError:
            If ``mdmc_reduce`` is none of ``None``, ``"samplewise"``, ``"global"``.
        ValueError:
            If ``reduce`` is set to ``"macro"`` and ``num_classes`` is not provided.
        ValueError:
            If ``num_classes`` is set
            and ``ignore_index`` is not in the range ``0`` <= ``ignore_index`` < ``num_classes``.

    Example:

        >>> from pytorch_lightning.metrics.classification import StatScores
        >>> preds  = torch.tensor([1, 0, 2, 1])
        >>> target = torch.tensor([1, 1, 2, 0])
        >>> stat_scores = StatScores(reduce='macro', num_classes=3)
        >>> stat_scores(preds, target)
        tensor([[0, 1, 2, 1, 1],
                [1, 1, 1, 1, 2],
                [1, 0, 3, 0, 1]])
        >>> stat_scores = StatScores(reduce='micro')
        >>> stat_scores(preds, target)
        tensor([2, 2, 6, 2, 4])

    """

    def __init__(
        self,
        threshold: float = 0.5,