o yi~@s*ddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZ d5ded d ed ed ed edeeddeddefddZ    d6dededededdeededefddZ     d7deded edeed!d"ededdeededefd#d$Z     d8deded%ededeed!deddeededefd&d'Z    d6dededededdeededefd(d)Z     d7deded edeed!d"ededdeededefd*d+Z     d8deded%ededeed!deddeededefd,d-Z    .    d9deded/ed0ded eed%eedeed!deedd"eedeededefd1d2Z    .    d9deded/ed0ded eed%eedeed!deedd"eedeededefd3d4Z dS):)OptionalN)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) _safe_divideglobalstat) precisionrecalltpfptnfnaverage)binarymicromacroweightednonemultidim_average)r samplewisereturnc Cs|dkr|n|}|dkrt|||S|dkrB|j|dkrdndd}|j|dkr+dndd}|j|dkr7dndd}t|||St|||}|dusQ|dkrS|S|d kr\||} nt|} t| || jd d d d S) Nrrrrr)dimrrT)keepdim)rsumtorch ones_like) rrrrrrr different_statscoreweightsr-k/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/precision_recall.py_precision_recall_reduce%s   r/?Tpredstarget threshold ignore_index validate_argsc CZ|rt|||t||||t||||\}}t|||\}}}} td|||| d|dS)a8 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. 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. Addtionally, 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 torchmetrics.functional.classification import binary_precision >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> binary_precision(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_precision >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_precision(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_precision >>> 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]], ... ] ... ) >>> binary_precision(preds, target, multidim_average='samplewise') tensor([0.4000, 0.0000]) rrrr rrrrr/ r1r2r3r r4r5rrrrr-r-r.binary_precisionA D r:rr# num_classes)rrrrtop_kc Cf|rt|||||t|||||t|||\}}t|||||||\}} } } td|| | | ||dS)alComputes `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. 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 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. 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 torchmetrics.functional.classification import multiclass_precision >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> multiclass_precision(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_precision(preds, target, num_classes=3, average=None) tensor([1.0000, 0.5000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_precision >>> 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], ... ]) >>> multiclass_precision(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_precision(preds, target, num_classes=3, average=None) tensor([1.0000, 0.5000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_precision >>> 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]]]) >>> multiclass_precision(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.3889, 0.2778]) >>> multiclass_precision(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[0.6667, 0.0000, 0.5000], [0.0000, 0.5000, 0.3333]]) rr7r r r r r/ r1r2r<rr=r r4r5rrrrr-r-r.multiclass_precisiona rA num_labelsc Cb|rt|||||t|||||t|||||\}}t|||\}} } } td|| | | ||dS)anComputes `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. 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. Addtionally, 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 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. 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 torchmetrics.functional.classification import multilabel_precision >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_precision(preds, target, num_labels=3) tensor(0.5000) >>> multilabel_precision(preds, target, num_labels=3, average=None) tensor([1.0000, 0.0000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_precision >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_precision(preds, target, num_labels=3) tensor(0.5000) >>> multilabel_precision(preds, target, num_labels=3, average=None) tensor([1.0000, 0.0000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_precision >>> 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]], ... ] ... ) >>> multilabel_precision(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.3333, 0.0000]) >>> multilabel_precision(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.0000]]) rr7r rrrr/ r1r2rCr3rr r4r5rrrrr-r-r.multilabel_precision _rGc Cr6)a 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. 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. Addtionally, 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 torchmetrics.functional.classification import binary_recall >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> binary_recall(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_recall >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_recall(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_recall >>> 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]], ... ] ... ) >>> binary_recall(preds, target, multidim_average='samplewise') tensor([0.6667, 0.0000]) rrr7r8r9r-r-r. binary_recall_r;rIc Cr>)aKComputes `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. 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 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. 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 torchmetrics.functional.classification import multiclass_recall >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> multiclass_recall(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_recall(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_recall >>> 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], ... ]) >>> multiclass_recall(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_recall(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_recall >>> 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]]]) >>> multiclass_recall(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.5000, 0.2778]) >>> multiclass_recall(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) rr7r?r@r-r-r.multiclass_recallrBrJc CrD)aComputes `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. 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. Addtionally, 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 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. 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 torchmetrics.functional.classification import multilabel_recall >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_recall(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_recall(preds, target, num_labels=3, average=None) tensor([1., 0., 1.]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_recall >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_recall(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_recall(preds, target, num_labels=3, average=None) tensor([1., 0., 1.]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_recall >>> 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]], ... ] ... ) >>> multilabel_recall(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.6667, 0.0000]) >>> multilabel_recall(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[1., 1., 0.], [0., 0., 0.]]) rr7rErFr-r-r.multilabel_recallrHrKrtask)r multiclass multilabelc C|dusJ|dkrt||||| | S|dkr0t|tsJt|ts%Jt||||||| | S|dkrFt|ts;Jt||||||| | Std|)aComputes `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 :func:`binary_precision`, :func:`multiclass_precision` and :func:`multilabel_precision` 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]) >>> precision(preds, target, task="multiclass", average='macro', num_classes=3) tensor(0.1667) >>> precision(preds, target, task="multiclass", average='micro', num_classes=3) tensor(0.2500) NrrMrN[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )r: isinstanceintrArG ValueError r1r2rLr3r<rCrr r=r4r5r-r-r.r}" !rc CrO)aComputes `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 :func:`binary_recall`, :func:`multiclass_recall` and :func:`multilabel_recall` 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]) >>> recall(preds, target, task="multiclass", average='macro', num_classes=3) tensor(0.3333) >>> recall(preds, target, task="multiclass", average='micro', num_classes=3) tensor(0.2500) NrrMrNrP)rIrQrRrJrKrSrTr-r-r.rrUr)r)r0rNT)rr#rNT)r0rrNT)r0NNrrr#NT)!typingrr(rtyping_extensionsr2torchmetrics.functional.classification.stat_scoresrrrrr r r r r rrrtorchmetrics.utilities.computerr/floatrRboolr:rArGrIrJrKrrr-r-r-r.s   8     P  o  j P  o  k      8