o .wi^@sddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZddlmZdd lmZdd lmZdd lmZmZesRgd ZGd dde ZGdddeZGdddeZGddde ZdS))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_hamming_distance_reduce)Metric)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)BinaryHammingDistance.plotMulticlassHammingDistance.plotMultilabelHammingDistance.plotc@seZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <d e fd d Z ddeee ee fdeed efddZd S)BinaryHammingDistancea Compute the average `Hamming distance`_ (also known as Hamming loss) for binary tasks. .. math:: \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il}) Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions, and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that tensor. 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: - ``bhd`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` arguments: - 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. 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 BinaryHammingDistance >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryHammingDistance() >>> metric(preds, target) tensor(0.3333) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryHammingDistance >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> metric = BinaryHammingDistance() >>> metric(preds, target) tensor(0.3333) Example (multidim tensors): >>> from torchmetrics.classification import BinaryHammingDistance >>> 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 = BinaryHammingDistance(multidim_average='samplewise') >>> metric(preds, target) tensor([0.6667, 0.8333]) Fis_differentiablehigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundreturncCs&|\}}}}t||||d|jdS)Compute metric.binaryaveragemultidim_average) _final_stater r"selftpfptnfnr*`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/hamming.pycomputeqszBinaryHammingDistance.computeNvalaxcC |||S)aMPlot 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 >>> # Example plotting a single value >>> from torch import rand, randint >>> from torchmetrics.classification import BinaryHammingDistance >>> metric = BinaryHammingDistance() >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.classification import BinaryHammingDistance >>> metric = BinaryHammingDistance() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) _plotr%r-r.r*r*r+plotv (rNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr,rrrrrr3r*r*r*r+r$s  F    rc@eZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed <d Z e ed <d efd dZ ddeeeeefdeed efddZdS)MulticlassHammingDistancea=Compute the average `Hamming distance`_ (also known as Hamming loss) for multiclass tasks. .. math:: \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il}) Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions, and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that tensor. 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: - ``mchd`` (:class:`~torch.Tensor`): A tensor 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 MulticlassHammingDistance >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> metric = MulticlassHammingDistance(num_classes=3) >>> metric(preds, target) tensor(0.1667) >>> mchd = MulticlassHammingDistance(num_classes=3, average=None) >>> mchd(preds, target) tensor([0.5000, 0.0000, 0.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassHammingDistance >>> 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 = MulticlassHammingDistance(num_classes=3) >>> metric(preds, target) tensor(0.1667) >>> mchd = MulticlassHammingDistance(num_classes=3, average=None) >>> mchd(preds, target) tensor([0.5000, 0.0000, 0.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassHammingDistance >>> 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 = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 0.7222]) >>> mchd = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise', average=None) >>> mchd(preds, target) tensor([[0.0000, 1.0000, 0.5000], [1.0000, 0.6667, 0.5000]]) FrrrrrrrClassplot_legend_namercCs(|\}}}}t|||||j|jdS)rr r#r r!r"r$r*r*r+r, sz!MulticlassHammingDistance.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 >>> # Example plotting a single value per class >>> from torch import randint >>> from torchmetrics.classification import MulticlassHammingDistance >>> metric = MulticlassHammingDistance(num_classes=3, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting a multiple values per class >>> from torch import randint >>> from torchmetrics.classification import MulticlassHammingDistance >>> metric = MulticlassHammingDistance(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) r0r2r*r*r+r3r4rr5r6r7r8r9rr:r;rrrr<rr@strrr,rrrrrr3r*r*r*r+r>s"  b     r>c@r=)MultilabelHammingDistanceaCompute the average `Hamming distance`_ (also known as Hamming loss) for multilabel tasks. .. math:: \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il}) Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions, and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that tensor. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int tensor 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: - ``mlhd`` (:class:`~torch.Tensor`): A tensor 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 MultilabelHammingDistance >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelHammingDistance(num_labels=3) >>> metric(preds, target) tensor(0.3333) >>> mlhd = MultilabelHammingDistance(num_labels=3, average=None) >>> mlhd(preds, target) tensor([0.0000, 0.5000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelHammingDistance >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelHammingDistance(num_labels=3) >>> metric(preds, target) tensor(0.3333) >>> mlhd = MultilabelHammingDistance(num_labels=3, average=None) >>> mlhd(preds, target) tensor([0.0000, 0.5000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelHammingDistance >>> 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 = MultilabelHammingDistance(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.6667, 0.8333]) >>> mlhd = MultilabelHammingDistance(num_labels=3, multidim_average='samplewise', average=None) >>> mlhd(preds, target) tensor([[0.5000, 0.5000, 1.0000], [1.0000, 1.0000, 0.5000]]) FrrrrrrrLabelr@rc Cs*|\}}}}t|||||j|jddS)rT)r!r" multilabelrAr$r*r*r+r,sz!MultilabelHammingDistance.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 and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelHammingDistance >>> metric = MultilabelHammingDistance(num_labels=3) >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelHammingDistance >>> metric = MultilabelHammingDistance(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) r0r2r*r*r+r3r4rr5rBr*r*r*r+rD;s"  `     rDc@seZdZdZ        ddedd ed d ed eed eedeeddeeddeedeede de de fddZ dS)HammingDistanceaCompute the average `Hamming distance`_ (also known as Hamming loss). .. math:: \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il}) Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions, and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that tensor. 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 :class:`~torchmetrics.classification.BinaryHammingDistance`, :class:`~torchmetrics.classification.MulticlassHammingDistance` and :class:`~torchmetrics.classification.MultilabelHammingDistance` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([[0, 1], [1, 1]]) >>> preds = tensor([[0, 1], [0, 1]]) >>> hamming_distance = HammingDistance(task="multilabel", num_labels=2) >>> hamming_distance(preds, target) tensor(0.2500) ?NmicroglobalTclstask)r multiclassrF threshold num_classes num_labelsr!)rImacroweightednoner")rJ samplewisetop_k ignore_index validate_argskwargsrc Kst|}|dus J| ||| d|tjkr!t|fi| S|tjkrNt|ts5tdt |dt|tsDtdt |dt |||fi| S|tj krlt|tsbtdt |dt |||fi| Std|d) zInitialize task metric.N)r"rWrXz+`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 `zTask z not supported!) rfrom_strupdateBINARYr MULTICLASS isinstanceint ValueErrortyper> MULTILABELrD) rLrMrOrPrQr!r"rVrWrXrYr*r*r+__new__s(        zHammingDistance.__new__)rHNNrIrJrKNT) r6r7r8r9rarr<rr_r:rr rcr*r*r*r+rGsF      rGN) collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser'torchmetrics.classification.stat_scoresr r r .torchmetrics.functional.classification.hammingr torchmetrics.metricr torchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr>rDrGr*r*r*r+s&        }