o yi>@sddlmZmZddlZddlmZddlmZddlmZm Z m Z ddl m Z ddl mZGdd d eZGd d d e ZGd d d e ZGdddZdS))AnyOptionalN)Tensor)Literal)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_specificity_reduce)Metricc@eZdZdZdefddZdS)BinarySpecificitya Computes `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 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, ...)``. 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: - ``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. 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 BinarySpecificity >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.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 = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.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 = 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 = BinarySpecificity(multidim_average='samplewise') >>> metric(preds, target) tensor([0.0000, 0.3333]) returncCs&|\}}}}t||||d|jdS)Nbinaryaveragemultidim_average) _final_stater rselftpfptnfnr[/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/specificity.pycomputeZszBinarySpecificity.computeN__name__ __module__ __qualname____doc__rrrrrrr s@r c@r )MulticlassSpecificityaComputes `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 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: - ``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)`` 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 MulticlassSpecificity >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.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 = 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 = 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 = 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 = 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]]) r cC(|\}}}}t|||||j|jdSNrrr rrrrrrrzMulticlassSpecificity.computeNrrrrrr!_s^r!c@r )MultilabelSpecificityaDComputes `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 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: - ``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)`` 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 MultilabelSpecificity >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = 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 = 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.]]) r cCr"r#r$rrrrr r%zMultilabelSpecificity.computeNrrrrrr&s\r&c@s|eZdZdZ        dded d ed eed eed eeddeeddeedeedede de fddZ dS) SpecificityaComputes `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 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:`BinarySpecificity`, :mod:`MulticlassSpecificity` and :mod:`MultilabelSpecificity` for the specific details of each argument influence and examples. Legacy Example: >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.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) ?NmicroglobalTtask)r multiclass multilabel threshold num_classes num_labelsr)r)macroweightednoner)r* samplewisetop_k ignore_index validate_argskwargsr c Ks|dusJ| t||| d|dkrt|fi| S|dkr8t|ts'Jt|ts.Jt|||fi| S|dkrMt|tsCJt|||fi| Std|)N)rr7r8rr-r.z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )updatedictr isinstanceintr!r& ValueError) clsr,r/r0r1rrr6r7r8r9rrr__new__=s zSpecificity.__new__)r(NNr)r*r+NT) rrrr rfloatrr=boolrr r@rrrrr'%sB      r')typingrrtorchrtyping_extensionsr'torchmetrics.classification.stat_scoresrrr2torchmetrics.functional.classification.specificityr torchmetrics.metricr r r!r&r'rrrrs     Fdb