i]\ddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZddlmZdd lmZdd lmZdd lmZmZesgd ZGd de ZGddeZGddeZGdde Zy))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_specificity_reduce)Metric)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)zBinarySpecificity.plotzMulticlassSpecificity.plotzMultilabelSpecificity.plotcneZdZUdZdZeed<dZeed<defdZ d d e e ee efd e e defd Zy) BinarySpecificitya% Compute `Specificity`_ for binary tasks. .. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}} Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives respectively. The metric is only proper defined when :math:`\text{TN} + \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`): An 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. Additionally, 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: - ``bs`` (: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. If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. 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 BinarySpecificity >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinarySpecificity() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinarySpecificity >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinarySpecificity() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinarySpecificity >>> 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 = BinarySpecificity(multidim_average='samplewise') >>> metric(preds, target) tensor([0.0000, 0.3333]) plot_lower_bound?plot_upper_boundreturncb|j\}}}}t||||d|jS)Compute metric.binaryaveragemultidim_average) _final_stater rselftpfptnfns |/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/classification/specificity.pycomputezBinarySpecificity.computefs4**,BB"2r2r8VZVkVkllNvalaxc&|j||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 BinarySpecificity >>> metric = BinarySpecificity() >>> 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 BinarySpecificity >>> metric = BinarySpecificity() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) _plotr"r*r+s r'plotzBinarySpecificity.plotkPzz#r""r)NN)__name__ __module__ __qualname____doc__rfloat__annotations__rrr(rrrrrr0r)r'rr sm@D"e!!e!mm _c(#E&(6*:":;<(#IQRZI[(# (#r)rc|eZdZUdZdZeed<dZeed<dZe ed<de fd Z dd e e e ee fd e edefd Zy )MulticlassSpecificityaLCompute `Specificity`_ for multiclass tasks. .. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}} Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives respectively. The metric is only proper defined when :math:`\text{TN} + \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: - ``mcs`` (: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)`` If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. Args: 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. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MulticlassSpecificity >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> metric = MulticlassSpecificity(num_classes=3) >>> metric(preds, target) tensor(0.8889) >>> mcs = MulticlassSpecificity(num_classes=3, average=None) >>> mcs(preds, target) tensor([1.0000, 0.6667, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassSpecificity >>> 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 = MulticlassSpecificity(num_classes=3) >>> metric(preds, target) tensor(0.8889) >>> mcs = MulticlassSpecificity(num_classes=3, average=None) >>> mcs(preds, target) tensor([1.0000, 0.6667, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassSpecificity >>> 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 = MulticlassSpecificity(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.7500, 0.6556]) >>> mcs = MulticlassSpecificity(num_classes=3, multidim_average='samplewise', average=None) >>> mcs(preds, target) tensor([[0.7500, 0.7500, 0.7500], [0.8000, 0.6667, 0.5000]]) rrrrClassplot_legend_namercv|j\}}}}t|||||j|jS)rrr r rrr!s r'r(zMulticlassSpecificity.computes8**,BB"2r2r4<>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassSpecificity >>> metric = MulticlassSpecificity(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 MulticlassSpecificity >>> metric = MulticlassSpecificity(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) r-r/s r'r0zMulticlassSpecificity.plotr1r)r2r3r4r5r6rr7r8rr=strrr(rrrrrr0r9r)r'r;r;sxaF"e!!e!#c#qq _c(#E&(6*:":;<(#IQRZI[(# (#r)r;c|eZdZUdZdZeed<dZeed<dZe ed<de fd Z dd e e e ee fd e edefd Zy )MultilabelSpecificityaCompute `Specificity`_ for multilabel tasks. .. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}} Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives respectively. The metric is only proper defined when :math:`\text{TN} + \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 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. Additionally, 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: - ``mls`` (: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)`` If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. Args: 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. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MultilabelSpecificity >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelSpecificity(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mls = MultilabelSpecificity(num_labels=3, average=None) >>> mls(preds, target) tensor([1., 1., 0.]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelSpecificity >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelSpecificity(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mls = MultilabelSpecificity(num_labels=3, average=None) >>> mls(preds, target) tensor([1., 1., 0.]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelSpecificity >>> 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 = MultilabelSpecificity(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.0000, 0.3333]) >>> mls = MultilabelSpecificity(num_labels=3, multidim_average='samplewise', average=None) >>> mls(preds, target) tensor([[0., 0., 0.], [0., 0., 1.]]) rrrrLabelr=rc x|j\}}}}t|||||j|jdS)rT)rr multilabelr?r!s r'r(zMultilabelSpecificity.computes?**,BB" BDLL4CXCXei  r)Nr*r+c&|j||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 rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelSpecificity >>> metric = MultilabelSpecificity(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 MultilabelSpecificity >>> metric = MultilabelSpecificity(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) r-r/s r'r0zMultilabelSpecificity.plotr1r)r2rAr9r)r'rDrD.sv\|"e!!e!#c#  _c(#E&(6*:":;<(#IQRZI[(# (#r)rDceZdZdZ ddeddeddedeedeed eed d eed d eedeede de de fdZ y) Specificitya|Compute `Specificity`_. .. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}} Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives respectively. The metric is only proper defined when :math:`\text{TN} + \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.BinarySpecificity`, :class:`~torchmetrics.classification.MulticlassSpecificity` and :class:`~torchmetrics.classification.MultilabelSpecificity` 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]) >>> specificity = Specificity(task="multiclass", average='macro', num_classes=3) >>> specificity(preds, target) tensor(0.6111) >>> specificity = Specificity(task="multiclass", average='micro', num_classes=3) >>> specificity(preds, target) tensor(0.6250) Nclstask)r multiclassrG threshold num_classes num_labelsr)micromacroweightednoner)global samplewisetop_k ignore_index validate_argskwargsrc  .tj|}|J| j||| d|tjk(r t |fi| S|tj k(r^t |tstdt|dt |tstdt|dt|||fi| S|tjk(r6t |tstdt|dt|||fi| Std|d)zInitialize task metric.)rrXrYz+`num_classes` is expected to be `int` but `z was passed.`z%`top_k` is expected to be `int` but `z*`num_labels` is expected to be `int` but `zTask z not supported!) rfrom_strupdateBINARYr MULTICLASS isinstanceint ValueErrortyper; MULTILABELrD) rKrLrNrOrPrrrWrXrYrZs r'__new__zSpecificity.__new__s'"**40+++ 0(*  %,, ,$Y9&9 9 %00 0k3/ #NtT_O`Naan!oppeS) #He Ub!cdd(eWOO O %00 0j#. #MdS]N^M__l!mnn(YR6R R5o677r))g?NNrQrUNT) r3r4r5r6rcrr7rraboolrrrer9r)r'rJrJs>%)$(KRFN &*"!8 - !8:;!8!8c] !8 SM !8 '"FGH !8#7+A#BC!8}!8sm!8!8!8 !8r)rJN) collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser 'torchmetrics.classification.stat_scoresr r r 2torchmetrics.functional.classification.specificityr torchmetrics.metricrtorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr;rDrJr9r)r'rtst%''%GppR&;@@ms#(s#lU#0U#pR#0R#j>8,>8r)