o .wiGN@sddlmZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZmZddlmZ   d.d ed ed ed edeeddeddededefddZ    d/dededededdeededefdd Z  !   d0deded"eedeed#dededdeededefd$d%Z  !   d1deded&ededeed#deddeededefd'd(Z    )   d2deded*ed+ded"eed&eeded#deddeedeededefd,d-ZdS)3)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)ClassificationTaskglobalFtpfptnfnaverage)binarymicromacroweightednonemultidim_average)r samplewise multilabeltop_kreturnc Cs|dkrt||||||S|dkr[|j|dkrdndd}|j|dkr)dndd}|rT|j|dkr7dndd}|j|dkrCdndd}t||||||St|||S|rjt||||||nt|||}t|||||||S)aReduce classification statistics into accuracy score. Args: tp: number of true positives fp: number of false positives tn: number of true negatives fn: number of false negatives average: Defines the reduction that is applied over labels. Should be one of the following: - ``binary``: for binary reduction - ``micro``: sum score over all classes/labels - ``macro``: salculate score for each class/label and average them - ``weighted``: calculates score for each class/label and computes weighted average using their support - ``"none"`` or ``None``: calculates score for each class/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. multilabel: If input is multilabel or not top_k: value for top-k accuracy, else 1 Returns: Accuracy score rrrrr)dim)rsumr) rrrrrr r"r#scorer(l/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/classification/accuracy.py_accuracy_reduce%s',r*?NTpredstarget threshold ignore_index validate_argsc CsX|rt|||t||||t||||\}}t|||\}}}} t|||| d|dS)a Compute `Accuracy`_ for binary tasks. .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. 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_accuracy >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> binary_accuracy(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_accuracy >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_accuracy(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_accuracy >>> 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_accuracy(preds, target, multidim_average='samplewise') tensor([0.3333, 0.1667]) r)rr )rrrrr*) r,r-r.r r/r0rrrrr(r(r)binary_accuracy[s C r1r num_classes)rrrrc Csj|rt|||||t|||||t|||\}}t|||p d||||\}} } } t|| | | |||dS)a]Compute `Accuracy`_ for multiclass tasks. .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. 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_accuracy >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> multiclass_accuracy(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_accuracy(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_accuracy >>> 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_accuracy(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_accuracy(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_accuracy >>> 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_accuracy(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.5000, 0.2778]) >>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) r)rr r#)r r r r r*) r,r-r2rr#r r/r0rrrrr(r(r)multiclass_accuracysb r3 num_labelsc Csb|rt|||||t|||||t|||||\}}t|||\}} } } t|| | | ||ddS)aCompute `Accuracy`_ for multilabel tasks. .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. 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_accuracy >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_accuracy(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_accuracy(preds, target, num_labels=3, average=None) tensor([1.0000, 0.5000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_accuracy >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_accuracy(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_accuracy(preds, target, num_labels=3, average=None) tensor([1.0000, 0.5000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_accuracy >>> 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_accuracy(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.3333, 0.1667]) >>> multilabel_accuracy(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.5000]]) T)rr r")r rrrr*) r,r-r4r.rr r/r0rrrrr(r(r)multilabel_accuracys ^r5rtask)r multiclassr"c Cst|}|tjkrt||||| | S|tjkrEt|ts)td|dt|t|ts:td|dt|t ||||||| | S|tj krft|ts[td|dt|t ||||||| | Std|)aCompute `Accuracy`_. .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. 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_accuracy`, :func:`~torchmetrics.functional.classification.multiclass_accuracy` and :func:`~torchmetrics.functional.classification.multilabel_accuracy` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 3]) >>> preds = tensor([0, 2, 1, 3]) >>> accuracy(preds, target, task="multiclass", num_classes=4) tensor(0.5000) >>> target = tensor([0, 1, 2]) >>> preds = tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]]) >>> accuracy(preds, target, task="multiclass", num_classes=3, top_k=2) tensor(0.6667) z;Optional arg `num_classes` must be type `int` when task is z. Got z5Optional arg `top_k` must be type `int` when task is z:Optional arg `num_labels` must be type `int` when task is zNot handled value: ) rfrom_strBINARYr1 MULTICLASS isinstanceint ValueErrortyper3 MULTILABELr5) r,r-r6r.r2r4rr r#r/r0r(r(r)accuracyxs, (      r@)rFr)r+rNT)NrrrNT)r+rrNT)r+NNrrrNT) typingrtorchrtyping_extensionsr2torchmetrics.functional.classification.stat_scoresrrrrr r r r r rrrtorchmetrics.utilities.computerrtorchmetrics.utilities.enumsrboolr<r*floatr1r3r5r@r(r(r(r)s  8    9 N  p  j