o yi|@sddlmZmZddlZddlmZddlmZddlmZm Z m Z ddl m Z ddl mZGdd d eZGd d d e ZGd d d e ZGdddeZGddde ZGddde ZGdddZGdddZdS))AnyOptionalN)Tensor)Literal)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_precision_recall_reduce)Metricc@HeZdZUdZdZeed<dZeeed<dZ eed<de fdd Z d S) BinaryPrecisiona Computes `Precision`_ for binary tasks: .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A int or float tensor of shape ``(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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``bp`` (:class:`~torch.Tensor`): 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. Args: 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. Example (preds is int tensor): >>> from torchmetrics.classification import BinaryPrecision >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryPrecision() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryPrecision >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinaryPrecision() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryPrecision >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = torch.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]], ... ] ... ) >>> metric = BinaryPrecision(multidim_average='samplewise') >>> metric(preds, target) tensor([0.4000, 0.0000]) Fis_differentiableThigher_is_betterfull_state_updatereturnc C(|\}}}}td||||d|jdS)N precisionbinaryaveragemultidim_average _final_stater rselftpfptnfnr`/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/precision_recall.pycompute^zBinaryPrecision.computeN __name__ __module__ __qualname____doc__r bool__annotations__rrrrr!rrrr r   @ r c@r ) MulticlassPrecisionaqComputes `Precision`_ for multiclass tasks. .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``. If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcp`` (:class:`~torch.Tensor`): 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)`` Args: num_classes: Integer specifing 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. Example (preds is int tensor): >>> from torchmetrics.classification import MulticlassPrecision >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> metric = MulticlassPrecision(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mcp = MulticlassPrecision(num_classes=3, average=None) >>> mcp(preds, target) tensor([1.0000, 0.5000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassPrecision >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.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], ... ]) >>> metric = MulticlassPrecision(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mcp = MulticlassPrecision(num_classes=3, average=None) >>> mcp(preds, target) tensor([1.0000, 0.5000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassPrecision >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = MulticlassPrecision(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.3889, 0.2778]) >>> mcp = MulticlassPrecision(num_classes=3, multidim_average='samplewise', average=None) >>> mcp(preds, target) tensor([[0.6667, 0.0000, 0.5000], [0.0000, 0.5000, 0.3333]]) Fr Trrrc C*|\}}}}td|||||j|jdSNrrrr rrrrrr r!zMulticlassPrecision.computeNr#rrrr r+e  ] r+c@r ) MultilabelPrecisionazComputes `Precision`_ for multilabel tasks. .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlp`` (:class:`~torch.Tensor`): 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)`` Args: num_labels: Integer specifing 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. Example (preds is int tensor): >>> from torchmetrics.classification import MultilabelPrecision >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelPrecision(num_labels=3) >>> metric(preds, target) tensor(0.5000) >>> mlp = MultilabelPrecision(num_labels=3, average=None) >>> mlp(preds, target) tensor([1.0000, 0.0000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelPrecision >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelPrecision(num_labels=3) >>> metric(preds, target) tensor(0.5000) >>> mlp = MultilabelPrecision(num_labels=3, average=None) >>> mlp(preds, target) tensor([1.0000, 0.0000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelPrecision >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = torch.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]], ... ] ... ) >>> metric = MultilabelPrecision(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.3333, 0.0000]) >>> mlp = MultilabelPrecision(num_labels=3, multidim_average='samplewise', average=None) >>> mlp(preds, target) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.0000]]) Fr Trrrc Cr,r-r.rrrr r!0r/zMultilabelPrecision.computeNr#rrrr r1r0r1c@r ) BinaryRecalla Computes `Recall`_ for binary tasks: .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` As output to ``forward`` and ``compute`` the metric returns the following output: - ``br`` (:class:`~torch.Tensor`): 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. Args: 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. Example (preds is int tensor): >>> from torchmetrics.classification import BinaryRecall >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryRecall() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryRecall >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinaryRecall() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryRecall >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = torch.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]], ... ] ... ) >>> metric = BinaryRecall(multidim_average='samplewise') >>> metric(preds, target) tensor([0.6667, 0.0000]) Fr Trrrc Cr)Nrecallrrrrrrr r!|r"zBinaryRecall.computeNr#rrrr r27r*r2c@r ) MulticlassRecallaNComputes `Recall`_ for multiclass tasks: .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)`` If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcr`` (:class:`~torch.Tensor`): 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)`` Args: num_classes: Integer specifing 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. Example (preds is int tensor): >>> from torchmetrics.classification import MulticlassRecall >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> metric = MulticlassRecall(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mcr = MulticlassRecall(num_classes=3, average=None) >>> mcr(preds, target) tensor([0.5000, 1.0000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassRecall >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.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], ... ]) >>> metric = MulticlassRecall(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mcr = MulticlassRecall(num_classes=3, average=None) >>> mcr(preds, target) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassRecall >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = MulticlassRecall(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 0.2778]) >>> mcr = MulticlassRecall(num_classes=3, multidim_average='samplewise', average=None) >>> mcr(preds, target) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) Fr Trrrc Cr,Nr3rr.rrrr r!r/zMulticlassRecall.computeNr#rrrr r4r0r4c@r ) MultilabelRecallaComputes `Recall`_ for multilabel tasks: .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlr`` (:class:`~torch.Tensor`): 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)`` Args: num_labels: Integer specifing 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. Example (preds is int tensor): >>> from torchmetrics.classification import MultilabelRecall >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelRecall(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mlr = MultilabelRecall(num_labels=3, average=None) >>> mlr(preds, target) tensor([1., 0., 1.]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelRecall >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelRecall(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mlr = MultilabelRecall(num_labels=3, average=None) >>> mlr(preds, target) tensor([1., 0., 1.]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelRecall >>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = torch.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]], ... ] ... ) >>> metric = MultilabelRecall(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.6667, 0.0000]) >>> mlr = MultilabelRecall(num_labels=3, multidim_average='samplewise', average=None) >>> mlr(preds, target) tensor([[1., 1., 0.], [0., 0., 0.]]) Fr Trrrc Cr,r5r.rrrr r!Mr/zMultilabelRecall.computeNr#rrrr r6s  \ r6c@|eZdZdZ        dded d ed eed eed eeddeeddeedeedede de fddZ dS) PrecisionaComputes `Precision`_: .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. 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 :mod:`BinaryPrecision`, :func:`MulticlassPrecision` and :func:`MultilabelPrecision` for the specific details of each argument influence and examples. Legacy Example: >>> import torch >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> precision = Precision(task="multiclass", average='macro', num_classes=3) >>> precision(preds, target) tensor(0.1667) >>> precision = Precision(task="multiclass", average='micro', num_classes=3) >>> precision(preds, target) tensor(0.2500) ?NmicroglobalTtaskr multiclass multilabel threshold num_classes num_labelsrr:macroweightednonerr; samplewisetop_k ignore_index validate_argskwargsrc K|dusJ| t||| d|dkrt|fi| S|dkr8t|ts'Jt|ts.Jt|||fi| S|dkrMt|tsCJt|||fi| Std|N)rrKrLrr?r@z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )updatedictr isinstanceintr+r1 ValueError clsr=rArBrCrrrJrKrLrMrrr __new__m zPrecision.__new__r9NNr:r;r<NT r$r%r&r'rfloatrrSr(rr rWrrrr r8TB      r8c@r7)RecallaComputes `Recall`_: .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. 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 :mod:`BinaryRecall`, :mod:`MulticlassRecall` and :mod:`MultilabelRecall` for the specific details of each argument influence and examples. Legacy Example: >>> import torch >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> recall = Recall(task="multiclass", average='macro', num_classes=3) >>> recall(preds, target) tensor(0.3333) >>> recall = Recall(task="multiclass", average='micro', num_classes=3) >>> recall(preds, target) tensor(0.2500) r9Nr:r;r<Tr=r>rArBrCrrDrrHrJrKrLrMrc KrNrO)rPrQr2rRrSr4r6rTrUrrr rWrXzRecall.__new__rYrZrrrr r]r\r])typingrrtorchrtyping_extensionsr'torchmetrics.classification.stat_scoresrrr7torchmetrics.functional.classification.precision_recallr torchmetrics.metricr r r+r1r2r4r6r8r]rrrr s     LiiLih6