:jHddlmZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZmZddlmZ d"deded ed ed eed d eddedefdZ d#dededed eddeededefdZ d$dededed eedded eddeededefdZ d%dedededed eedd eddeededefdZ d&dededed dedeedeed eedd eeddeedeededefd!Zy)')Optional)Tensor)Literal) "_binary_stat_scores_arg_validation_binary_stat_scores_format%_binary_stat_scores_tensor_validation_binary_stat_scores_update&_multiclass_stat_scores_arg_validation_multiclass_stat_scores_format)_multiclass_stat_scores_tensor_validation_multiclass_stat_scores_update&_multilabel_stat_scores_arg_validation_multilabel_stat_scores_format)_multilabel_stat_scores_tensor_validation_multilabel_stat_scores_update)_adjust_weights_safe_divide _safe_divide)ClassificationTasktpfptnfnaverage)binarymicromacroweightednonemultidim_average)global samplewise multilabelreturnc|dk(rt|||zS|dk(rA|j|dk(rdnd}|j|dk(rdnd}t|||zSt|||z}t||||||S)Nrrr r)dim)rsumr)rrrrrrr"specificity_scores /home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/classification/specificity.py_specificity_reducer*%s(BR((' VV-9qV A VV-9qV ABR(($Rb1 &'8':rSUWY ZZNpredstarget threshold ignore_index validate_argsc|rt|||t||||t||||\}}t|||\}}}} t |||| d|S)a 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 respecitively. 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_specificity >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> binary_specificity(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_specificity >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_specificity(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_specificity >>> 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_specificity(preds, target, multidim_average='samplewise') tensor([0.0000, 0.3333]) rrr)rrrr r*) r,r-r.rr/r0rrrrs r)binary_specificityr39seD*96F U-eV=M|\.ufiVME6/v?OPNBB r2r2xRb ccr+ num_classes)rrrrtop_kc |rt|||||t|||||t|||\}}t|||||||\}} } } t || | | ||S)aCompute `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. 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_specificity >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> multiclass_specificity(preds, target, num_classes=3) tensor(0.8889) >>> multiclass_specificity(preds, target, num_classes=3, average=None) tensor([1.0000, 0.6667, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_specificity >>> 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_specificity(preds, target, num_classes=3) tensor(0.8889) >>> multiclass_specificity(preds, target, num_classes=3, average=None) tensor([1.0000, 0.6667, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_specificity >>> 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_specificity(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.7500, 0.6556]) >>> multiclass_specificity(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[0.7500, 0.7500, 0.7500], [0.8000, 0.6667, 0.5000]]) r2)r r r r r*) r,r-r4rr5rr/r0rrrrs r)multiclass_specificityr7sxB.{E7L\^jk1%N^`lm25&%HME63 v{E74DlNBB r2r2wQa bbr+ num_labelsc |rt|||||t|||||t|||||\}}t|||\}} } } t || | | ||dS)aCompute `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. 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_specificity >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_specificity(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_specificity(preds, target, num_labels=3, average=None) tensor([1., 1., 0.]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_specificity >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_specificity(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_specificity(preds, target, num_labels=3, average=None) tensor([1., 1., 0.]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_specificity >>> 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_specificity(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.0000, 0.3333]) >>> multilabel_specificity(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[0., 0., 0.], [0., 0., 1.]]) T)rrr")rrrrr*) r,r-r8r.rrr/r0rrrrs r)multilabel_specificityr:ssz.z9gO_amn1%M]_kl25&*iYefME63E6CSTNBB r2r2wQanr ssr+task)r multiclassr"c tj|}|J|tjk(rt||||| | S|tjk(rbt |t stdt|dt |t stdt|dt||||||| | S|tjk(r:t |t stdt|dt||||||| | Std|)adCompute `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 :func:`~torchmetrics.functional.classification.binary_specificity`, :func:`~torchmetrics.functional.classification.multiclass_specificity` and :func:`~torchmetrics.functional.classification.multilabel_specificity` for the specific details of each argument influence and examples. LegacyExample: >>> from torch import tensor >>> preds = tensor([2, 0, 2, 1]) >>> target = tensor([1, 1, 2, 0]) >>> specificity(preds, target, task="multiclass", average='macro', num_classes=3) tensor(0.6111) >>> specificity(preds, target, task="multiclass", average='micro', num_classes=3) tensor(0.6250) z+`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 `zNot handled value: ) rfrom_strBINARYr3 MULTICLASS isinstanceint ValueErrortyper7 MULTILABELr:) r,r-r;r.r4r8rrr5r/r0s r) specificityrFSs-J  & &t ,D  '' ' !(((!%rQs%    U;9A[[[ [  [ gLM N [ 45 [[ [.8@"& Gd Gd GdGd45 Gd 3- Gd  Gd Gd\HO8@"&hc hc hchcgBC D hc  hc 45 hc3-hchc hc^GN8@"&bt bt btbt bt gBC D bt 45 bt3-btbt btR!% $GNBJ"&73 73 73 6 773 73 # 73  73gBC D73w'=>?73 C=733-7373 73r+