image-match/python/py/Lib/site-packages/torchmetrics/functional/classification/hamming.py

# Copyright The Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional

from torch import Tensor
from typing_extensions import Literal

from torchmetrics.functional.classification.stat_scores import (
    _binary_stat_scores_arg_validation,
    _binary_stat_scores_format,
    _binary_stat_scores_tensor_validation,
    _binary_stat_scores_update,
    _multiclass_stat_scores_arg_validation,
    _multiclass_stat_scores_format,
    _multiclass_stat_scores_tensor_validation,
    _multiclass_stat_scores_update,
    _multilabel_stat_scores_arg_validation,
    _multilabel_stat_scores_format,
    _multilabel_stat_scores_tensor_validation,
    _multilabel_stat_scores_update,
)
from torchmetrics.utilities.compute import _adjust_weights_safe_divide, _safe_divide
from torchmetrics.utilities.enums import ClassificationTask


def _hamming_distance_reduce(
    tp: Tensor,
    fp: Tensor,
    tn: Tensor,
    fn: Tensor,
    average: Optional[Literal["binary", "micro", "macro", "weighted", "none"]],
    multidim_average: Literal["global", "samplewise"] = "global",
    multilabel: bool = False,
) -> Tensor:
    """Reduce classification statistics into hamming distance.

    Args:
        tp: number of true positives
        fp: number of false positives
        tn: number of true negatives
        fn: number of false negatives
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``binary``: for binary reduction
            - ``micro``: sum score over all classes/labels
            - ``macro``: salculate score for each class/label and average them
            - ``weighted``: calculates score for each class/label and computes weighted average using their support
            - ``"none"`` or ``None``: calculates score for each class/label and applies no reduction

        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.

        multilabel: If input is multilabel or not

    """
    if average == "binary":
        return 1 - _safe_divide(tp + tn, tp + fp + tn + fn)
    if average == "micro":
        tp = tp.sum(dim=0 if multidim_average == "global" else 1)
        fn = fn.sum(dim=0 if multidim_average == "global" else 1)
        if multilabel:
            fp = fp.sum(dim=0 if multidim_average == "global" else 1)
            tn = tn.sum(dim=0 if multidim_average == "global" else 1)
            return 1 - _safe_divide(tp + tn, tp + tn + fp + fn)
        return 1 - _safe_divide(tp, tp + fn)

    score = 1 - _safe_divide(tp + tn, tp + tn + fp + fn) if multilabel else 1 - _safe_divide(tp, tp + fn)
    return _adjust_weights_safe_divide(score, average, multilabel, tp, fp, fn)


def binary_hamming_distance(
    preds: Tensor,
    target: Tensor,
    threshold: float = 0.5,
    multidim_average: Literal["global", "samplewise"] = "global",
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Compute the average `Hamming distance`_ (also known as Hamming loss) for binary tasks.

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

    Accepts the following input tensors:

    - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside
      [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Additionally,
      we convert to int tensor with thresholding using the value in ``threshold``.
    - ``target`` (int tensor): ``(N, ...)``

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        threshold: Threshold for transforming probability to binary {0,1} predictions
        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average``
        is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample.

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import binary_hamming_distance
        >>> target = tensor([0, 1, 0, 1, 0, 1])
        >>> preds = tensor([0, 0, 1, 1, 0, 1])
        >>> binary_hamming_distance(preds, target)
        tensor(0.3333)

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import binary_hamming_distance
        >>> target = tensor([0, 1, 0, 1, 0, 1])
        >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
        >>> binary_hamming_distance(preds, target)
        tensor(0.3333)

    Example (multidim tensors):
        >>> from torchmetrics.functional.classification import binary_hamming_distance
        >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
        >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
        ...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
        >>> binary_hamming_distance(preds, target, multidim_average='samplewise')
        tensor([0.6667, 0.8333])

    """
    if validate_args:
        _binary_stat_scores_arg_validation(threshold, multidim_average, ignore_index)
        _binary_stat_scores_tensor_validation(preds, target, multidim_average, ignore_index)
    preds, target = _binary_stat_scores_format(preds, target, threshold, ignore_index)
    tp, fp, tn, fn = _binary_stat_scores_update(preds, target, multidim_average)
    return _hamming_distance_reduce(tp, fp, tn, fn, average="binary", multidim_average=multidim_average)


def multiclass_hamming_distance(
    preds: Tensor,
    target: Tensor,
    num_classes: int,
    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
    top_k: int = 1,
    multidim_average: Literal["global", "samplewise"] = "global",
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Compute the average `Hamming distance`_ (also known as Hamming loss) for multiclass tasks.

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

    Accepts the following input tensors:

    - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point
      we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into
      an int tensor.
    - ``target`` (int tensor): ``(N, ...)``

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_classes: Integer specifying the number of classes
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum statistics over all labels
            - ``macro``: Calculate statistics for each label and average them
            - ``weighted``: calculates statistics for each label and computes weighted average using their support
            - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction

        top_k:
            Number of highest probability or logit score predictions considered to find the correct label.
            Only works when ``preds`` contain probabilities/logits.
        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        The returned shape depends on the ``average`` and ``multidim_average`` arguments:

        - If ``multidim_average`` is set to ``global``:

          - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
          - If ``average=None/'none'``, the shape will be ``(C,)``

        - If ``multidim_average`` is set to ``samplewise``:

          - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
          - If ``average=None/'none'``, the shape will be ``(N, C)``

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import multiclass_hamming_distance
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([2, 1, 0, 1])
        >>> multiclass_hamming_distance(preds, target, num_classes=3)
        tensor(0.1667)
        >>> multiclass_hamming_distance(preds, target, num_classes=3, average=None)
        tensor([0.5000, 0.0000, 0.0000])

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import multiclass_hamming_distance
        >>> target = tensor([2, 1, 0, 0])
        >>> preds = tensor([[0.16, 0.26, 0.58],
        ...                 [0.22, 0.61, 0.17],
        ...                 [0.71, 0.09, 0.20],
        ...                 [0.05, 0.82, 0.13]])
        >>> multiclass_hamming_distance(preds, target, num_classes=3)
        tensor(0.1667)
        >>> multiclass_hamming_distance(preds, target, num_classes=3, average=None)
        tensor([0.5000, 0.0000, 0.0000])

    Example (multidim tensors):
        >>> from torchmetrics.functional.classification import multiclass_hamming_distance
        >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
        >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
        >>> multiclass_hamming_distance(preds, target, num_classes=3, multidim_average='samplewise')
        tensor([0.5000, 0.7222])
        >>> multiclass_hamming_distance(preds, target, num_classes=3, multidim_average='samplewise', average=None)
        tensor([[0.0000, 1.0000, 0.5000],
                [1.0000, 0.6667, 0.5000]])

    """
    if validate_args:
        _multiclass_stat_scores_arg_validation(num_classes, top_k, average, multidim_average, ignore_index)
        _multiclass_stat_scores_tensor_validation(preds, target, num_classes, multidim_average, ignore_index)
    preds, target = _multiclass_stat_scores_format(preds, target, top_k)
    tp, fp, tn, fn = _multiclass_stat_scores_update(
        preds, target, num_classes, top_k, average, multidim_average, ignore_index
    )
    return _hamming_distance_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average)


def multilabel_hamming_distance(
    preds: Tensor,
    target: Tensor,
    num_labels: int,
    threshold: float = 0.5,
    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
    multidim_average: Literal["global", "samplewise"] = "global",
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Compute the average `Hamming distance`_ (also known as Hamming loss) for multilabel tasks.

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

    Accepts the following input tensors:

    - ``preds`` (int or float tensor): ``(N, C, ...)``. If preds is a floating point tensor with values outside
      [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Additionally,
      we convert to int tensor with thresholding using the value in ``threshold``.
    - ``target`` (int tensor): ``(N, C, ...)``

    Args:
        preds: Tensor with predictions
        target: Tensor with true labels
        num_labels: Integer specifying the number of labels
        threshold: Threshold for transforming probability to binary (0,1) predictions
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum statistics over all labels
            - ``macro``: Calculate statistics for each label and average them
            - ``weighted``: calculates statistics for each label and computes weighted average using their support
            - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction

        multidim_average:
            Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

            - ``global``: Additional dimensions are flatted along the batch dimension
            - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
              The statistics in this case are calculated over the additional dimensions.

        ignore_index:
            Specifies a target value that is ignored and does not contribute to the metric calculation
        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.

    Returns:
        The returned shape depends on the ``average`` and ``multidim_average`` arguments:

        - If ``multidim_average`` is set to ``global``:

          - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
          - If ``average=None/'none'``, the shape will be ``(C,)``

        - If ``multidim_average`` is set to ``samplewise``:

          - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
          - If ``average=None/'none'``, the shape will be ``(N, C)``

    Example (preds is int tensor):
        >>> from torch import tensor
        >>> from torchmetrics.functional.classification import multilabel_hamming_distance
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
        >>> multilabel_hamming_distance(preds, target, num_labels=3)
        tensor(0.3333)
        >>> multilabel_hamming_distance(preds, target, num_labels=3, average=None)
        tensor([0.0000, 0.5000, 0.5000])

    Example (preds is float tensor):
        >>> from torchmetrics.functional.classification import multilabel_hamming_distance
        >>> target = tensor([[0, 1, 0], [1, 0, 1]])
        >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
        >>> multilabel_hamming_distance(preds, target, num_labels=3)
        tensor(0.3333)
        >>> multilabel_hamming_distance(preds, target, num_labels=3, average=None)
        tensor([0.0000, 0.5000, 0.5000])

    Example (multidim tensors):
        >>> from torchmetrics.functional.classification import multilabel_hamming_distance
        >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
        >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
        ...                 [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
        >>> multilabel_hamming_distance(preds, target, num_labels=3, multidim_average='samplewise')
        tensor([0.6667, 0.8333])
        >>> multilabel_hamming_distance(preds, target, num_labels=3, multidim_average='samplewise', average=None)
        tensor([[0.5000, 0.5000, 1.0000],
                [1.0000, 1.0000, 0.5000]])

    """
    if validate_args:
        _multilabel_stat_scores_arg_validation(num_labels, threshold, average, multidim_average, ignore_index)
        _multilabel_stat_scores_tensor_validation(preds, target, num_labels, multidim_average, ignore_index)
    preds, target = _multilabel_stat_scores_format(preds, target, num_labels, threshold, ignore_index)
    tp, fp, tn, fn = _multilabel_stat_scores_update(preds, target, multidim_average)
    return _hamming_distance_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average, multilabel=True)


def hamming_distance(
    preds: Tensor,
    target: Tensor,
    task: Literal["binary", "multiclass", "multilabel"],
    threshold: float = 0.5,
    num_classes: Optional[int] = None,
    num_labels: Optional[int] = None,
    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
    multidim_average: Optional[Literal["global", "samplewise"]] = "global",
    top_k: Optional[int] = 1,
    ignore_index: Optional[int] = None,
    validate_args: bool = True,
) -> Tensor:
    r"""Compute the average `Hamming distance`_ (also known as Hamming loss).

    .. math::
        \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

    Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
    and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
    tensor.

    This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
    :func:`~torchmetrics.functional.classification.binary_hamming_distance`,
    :func:`~torchmetrics.functional.classification.multiclass_hamming_distance` and
    :func:`~torchmetrics.functional.classification.multilabel_hamming_distance` for
    the specific details of each argument influence and examples.

    Legacy Example:
        >>> from torch import tensor
        >>> target = tensor([[0, 1], [1, 1]])
        >>> preds = tensor([[0, 1], [0, 1]])
        >>> hamming_distance(preds, target, task="binary")
        tensor(0.2500)

    """
    task = ClassificationTask.from_str(task)
    assert multidim_average is not None  # noqa: S101  # needed for mypy
    if task == ClassificationTask.BINARY:
        return binary_hamming_distance(preds, target, threshold, multidim_average, ignore_index, validate_args)
    if task == ClassificationTask.MULTICLASS:
        if not isinstance(num_classes, int):
            raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
        if not isinstance(top_k, int):
            raise ValueError(f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`")
        return multiclass_hamming_distance(
            preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
        )
    if task == ClassificationTask.MULTILABEL:
        if not isinstance(num_labels, int):
            raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
        return multilabel_hamming_distance(
            preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
        )
    raise ValueError(f"Not handled value: {task}")