a  dU@s ddlmZmZmZmZddlmZddlmZddl m Z ddl m Z m Z mZddlmZddlmZddlmZdd lmZdd lmZmZesgd ZGd d d e ZGddde ZGdddeZGddde ZGddde ZGdddeZ Gddde Z!Gddde Z"dS))AnyOptionalSequenceUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_precision_recall_reduce)Metric)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)BinaryPrecision.plotMulticlassPrecision.plotMultilabelPrecision.plotBinaryRecall.plotMulticlassRecall.plotMultilabelRecall.plotc@seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <e d d d Zdeee ee feeedddZdS)BinaryPrecisionaB Compute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is encountered a score of 0 is returned. 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 torch import tensor >>> from torchmetrics.classification import BinaryPrecision >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = 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]]]) >>> metric = BinaryPrecision(multidim_average='samplewise') >>> metric(preds, target) tensor([0.4000, 0.0000]) Fis_differentiableThigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundreturnc Cs(|\}}}}td||||d|jdS)Compute metric. precisionbinaryaveragemultidim_average _final_stater r'selftpfptnfnr0u/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/classification/precision_recall.pycomputekszBinaryPrecision.computeNvalaxr!cCs |||S)a5Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryPrecision >>> metric = BinaryPrecision() >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryPrecision >>> metric = BinaryPrecision() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) Z_plotr+r4r5r0r0r1plotrs(r)NN__name__ __module__ __qualname____doc__rbool__annotations__rrrrfloatrrr2rrrrr8r0r0r0r1r&s >    rc@seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <ed ddZdeeeeefeeedddZdS)MulticlassPrecisionacCompute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is encountered for any class, the metric for that class will be set to 0 and the overall metric may therefore be affected in turn. 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 torch import tensor >>> from torchmetrics.classification import MulticlassPrecision >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = 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]]) >>> 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 = 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]]]) >>> 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]]) FrTrrrrrrClassplot_legend_namer c Cs*|\}}}}td|||||j|jdS)r"r#r%r)r r&r'r*r0r0r1r2szMulticlassPrecision.computeNr3cCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassPrecision >>> metric = MulticlassPrecision(num_classes=3, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassPrecision >>> metric = MulticlassPrecision(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) r6r7r0r0r1r8 s(r)NNr:r;r<r=rr>r?rrrrr@rrCstrrr2rrrrr8r0r0r0r1rAs _     rAc@seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <ed ddZdeeeeefeeedddZdS)MultilabelPrecisiona"Compute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is encountered for any label, the metric for that label will be set to 0 and the overall metric may therefore be affected in turn. 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 torch import tensor >>> from torchmetrics.classification import MultilabelPrecision >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = 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]]]) >>> 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]]) FrTrrrrrrLabelrCr c Cs,|\}}}}td|||||j|jddS)r"r#Tr&r' multilabelrDr*r0r0r1r2szMultilabelPrecision.computeNr3cCs |||S)ayPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelPrecision >>> metric = MultilabelPrecision(num_labels=3) >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelPrecision >>> metric = MultilabelPrecision(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) r6r7r0r0r1r8s(r)NNrEr0r0r0r1rG6s \     rGc@seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <e d d d Zdeee ee feeedddZdS) BinaryRecalla1 Compute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is encountered a score of 0 is returned. 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 torch import tensor >>> from torchmetrics.classification import BinaryRecall >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = 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]]]) >>> metric = BinaryRecall(multidim_average='samplewise') >>> metric(preds, target) tensor([0.6667, 0.0000]) FrTrrrrrrr c Cs(|\}}}}td||||d|jdS)r"recallr$r%r(r*r0r0r1r2szBinaryRecall.computeNr3cCs |||S)a)Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryRecall >>> metric = BinaryRecall() >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryRecall >>> metric = BinaryRecall() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) r6r7r0r0r1r8s(r)NNr9r0r0r0r1rKs >    rKc@seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <ed ddZdeeeeefeeedddZdS)MulticlassRecalla?Compute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is encountered for any class, the metric for that class will be set to 0 and the overall metric may therefore be affected in turn. 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 torch import tensor >>> from torchmetrics.classification import MulticlassRecall >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = 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]]) >>> 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 = 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]]]) >>> 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]]) FrTrrrrrrrBrCr c Cs*|\}}}}td|||||j|jdS)r"rLr%rDr*r0r0r1r2szMulticlassRecall.computeNr3cCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassRecall >>> metric = MulticlassRecall(num_classes=3, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassRecall >>> metric = MulticlassRecall(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) r6r7r0r0r1r8s(r)NNrEr0r0r0r1rMCs ^     rMc@seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <ed ddZdeeeeefeeedddZdS)MultilabelRecallaCompute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is encountered for any label, the metric for that label will be set to 0 and the overall metric may therefore be affected in turn. 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 torch import tensor >>> from torchmetrics.classification import MultilabelRecall >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = 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]]]) >>> 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.]]) FrTrrrrrrrHrCr c Cs,|\}}}}td|||||j|jddS)r"rLTrIrDr*r0r0r1r2>szMultilabelRecall.computeNr3cCs |||S)amPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelRecall >>> metric = MultilabelRecall(num_labels=3) >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelRecall >>> metric = MultilabelRecall(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) r6r7r0r0r1r8Es(r)NNrEr0r0r0r1rNs [     rNc @sXeZdZdZdedeeeeeeed eed eeeeee e d d d Z dS) PrecisionadCompute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may therefore be affected in turn. 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 :class:`~torchmetrics.classification.BinaryPrecision`, :class:`~torchmetrics.classification.MulticlassPrecision` and :class:`~torchmetrics.classification.MultilabelPrecision` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> preds = tensor([2, 0, 2, 1]) >>> target = 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) ?NmicroglobalTr$Z multiclassrJrQmacroZweightednonerRZ samplewise task threshold num_classes num_labelsr&r'top_k ignore_index validate_argskwargsr!c Ks|dus J| ||| dt|}|tjkrBt|fi| S|tjkrt|tsjtdt |dt|tstdt |dt |||fi| S|tj krt|tstdt |dt |||fi| Std|ddS) Initialize task metric.Nr'r_r`+`num_classes` is expected to be `int` but ` was passed.`%`top_k` is expected to be `int` but `*`num_labels` is expected to be `int` but `zTask z not supported!) updaterfrom_strBINARYr MULTICLASS isinstanceint ValueErrortyperA MULTILABELrG clsrZr[r\r]r&r'r^r_r`rar0r0r1__new__s$         zPrecision.__new__)rPNNrQrRrSNT r:r;r<r=rr@rrmr>rr rsr0r0r0r1rOps,  rOc @sXeZdZdZdedeeeeeeed eed eeeeee e d d d Z dS)RecallaCCompute `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 respectively. The metric is only proper defined when :math:`\text{TP} + \text{FN} \neq 0`. If this case is encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may therefore be affected in turn. 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 :class:`~torchmetrics.classification.BinaryRecall`, :class:`~torchmetrics.classification.MulticlassRecall` and :class:`~torchmetrics.classification.MultilabelRecall` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> preds = tensor([2, 0, 2, 1]) >>> target = 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) rPNrQrRrSTrTrUrXrYc Kst|}|dusJ| ||| d|tjkrBt|fi| S|tjkrt|tsjtdt |dt|tstdt |dt |||fi| S|tj krt|tstdt |dt |||fi| SdS)rbNrcrdrerfrg) rrirhrjrKrkrlrmrnrorMrprNrqr0r0r1rss$         zRecall.__new__)rPNNrQrRrSNTrtr0r0r0r1rus,  ruN)#typingrrrrZtorchrZtyping_extensionsrZ torchmetrics.classification.baserZ'torchmetrics.classification.stat_scoresr r r Z7torchmetrics.functional.classification.precision_recallr Ztorchmetrics.metricr Ztorchmetrics.utilities.enumsrZtorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rrArGrKrMrNrOrur0r0r0r1s.        ww?