o .wip[@sddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZddlmZdd lmZdd lmZdd lmZmZesRgd ZGd dde ZGdddeZGdddeZGddde ZdS))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_accuracy_reduce)Metric)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)BinaryAccuracy.plotMulticlassAccuracy.plotMultilabelAccuracy.plotc@seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d e fd d Z ddeee ee fdeed efddZdS)BinaryAccuracya 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. 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: - ``acc`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, 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 BinaryAccuracy >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryAccuracy() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryAccuracy >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinaryAccuracy() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryAccuracy >>> 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 = BinaryAccuracy(multidim_average='samplewise') >>> metric(preds, target) tensor([0.3333, 0.1667]) Fis_differentiableThigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundreturncCs&|\}}}}t||||d|jdS)DCompute accuracy based on inputs passed in to ``update`` previously.binary)averagemultidim_average) _final_stater r!selftpfptnfnr)a/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/accuracy.pycomputehszBinaryAccuracy.computeNvalaxcC |||S)a1Plot 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 BinaryAccuracy >>> metric = BinaryAccuracy() >>> 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 BinaryAccuracy >>> metric = BinaryAccuracy() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) _plotr$r,r-r)r)r*plotm (rNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr+rrrrrr2r)r)r)r*r s  A    rc@eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e ed <d efddZ ddeeeeefdeed efddZdS)MulticlassAccuracyaCompute `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. 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: - ``mca`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose 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 MulticlassAccuracy >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> metric = MulticlassAccuracy(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mca = MulticlassAccuracy(num_classes=3, average=None) >>> mca(preds, target) tensor([0.5000, 1.0000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassAccuracy >>> 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 = MulticlassAccuracy(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mca = MulticlassAccuracy(num_classes=3, average=None) >>> mca(preds, target) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassAccuracy >>> 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 = MulticlassAccuracy(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 0.2778]) >>> mca = MulticlassAccuracy(num_classes=3, multidim_average='samplewise', average=None) >>> mca(preds, target) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) FrTrrrrrrClassplot_legend_namerc Cs,|\}}}}t|||||j|j|jdS)r)r r!top_k)r"r r r!r@r#r)r)r*r+szMulticlassAccuracy.computeNr,r-cCr.)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 randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassAccuracy >>> metric = MulticlassAccuracy(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 MulticlassAccuracy >>> metric = MulticlassAccuracy(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/r1r)r)r*r2r3rr4r5r6r7r8rr9r:rrrr;rr?strrr+rrrrrr2r)r)r)r*r="  a     r=c@r<)MultilabelAccuracya;Compute `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. 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: - ``mla`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose 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 MultilabelAccuracy >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelAccuracy(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mla = MultilabelAccuracy(num_labels=3, average=None) >>> mla(preds, target) tensor([1.0000, 0.5000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelAccuracy >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelAccuracy(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mla = MultilabelAccuracy(num_labels=3, average=None) >>> mla(preds, target) tensor([1.0000, 0.5000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelAccuracy >>> 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]], ... ] ... ) >>> mla = MultilabelAccuracy(num_labels=3, multidim_average='samplewise') >>> mla(preds, target) tensor([0.3333, 0.1667]) >>> mla = MultilabelAccuracy(num_labels=3, multidim_average='samplewise', average=None) >>> mla(preds, target) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.5000]]) FrTrrrrrrLabelr?rc Cs*|\}}}}t|||||j|jddS)rT)r r! multilabel)r"r r r!r#r)r)r*r+szMultilabelAccuracy.computeNr,r-cCr.)anPlot 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 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 MultilabelAccuracy >>> metric = MultilabelAccuracy(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 MultilabelAccuracy >>> metric = MultilabelAccuracy(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/r1r)r)r*r2r3rr4rAr)r)r)r*rD3rCrDc@seZdZdZ        ddedd ed d ed eed eedeeddeddeedeede de de fddZ dS)AccuracyaCompute `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 module 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.BinaryAccuracy`, :class:`~torchmetrics.classification.MulticlassAccuracy` and :class:`~torchmetrics.classification.MultilabelAccuracy` 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 = Accuracy(task="multiclass", num_classes=4) >>> accuracy(preds, target) 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 = Accuracy(task="multiclass", num_classes=3, top_k=2) >>> accuracy(preds, target) tensor(0.6667) ?NmicroglobalTclstask)r multiclassrF threshold num_classes num_labelsr )rImacroweightednoner!)rJ samplewiser@ ignore_index validate_argskwargsrc Kst|}| ||| d|tjkrt|fi| S|tjkrLt|ts1td|dt |t|tsBtd|dt |t |||fi| S|tj krlt|tsbtd|dt |t |||fi| Std|)zInitialize task metric.)r!rVrWz;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_strupdateBINARYr MULTICLASS isinstanceint ValueErrortyper= MULTILABELrD) rLrMrOrPrQr r!r@rVrWrXr)r)r*__new__s.       zAccuracy.__new__)rHNNrIrJrKNT) r5r6r7r8r`rr;rr^r9rr rbr)r)r)r*rGsF      rGN) collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser'torchmetrics.classification.stat_scoresr r r /torchmetrics.functional.classification.accuracyr torchmetrics.metricr torchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr=rDrGr)r)r)r*s&        x