o yimC@sddlmZmZddlZddlmZddlmZddlmZm Z m Z ddl m Z ddl mZGdd d eZGd d d e ZGd d d e ZGdddZdS))AnyOptionalN)Tensor)Literal)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)_hamming_distance_reduce)Metricc@DeZdZUdZdZeed<dZeed<dZeed<de fddZ d S) BinaryHammingDistancea" Computes 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. Addtionally, 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. 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 torchmetrics.classification import BinaryHammingDistance >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.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 = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.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 = 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]], ... ] ... ) >>> metric = BinaryHammingDistance(multidim_average='samplewise') >>> metric(preds, target) tensor([0.6667, 0.8333]) Fis_differentiablehigher_is_betterfull_state_updatereturncCs&|\}}}}t||||d|jdS)Nbinaryaveragemultidim_average) _final_stater rselftpfptnfnrW/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/hamming.pycomputecszBinaryHammingDistance.computeN __name__ __module__ __qualname____doc__r bool__annotations__rrrrrrrrr s  E  r c@r ) MulticlassHammingDistancea]Computes 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)`` Args: 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. Example (preds is int tensor): >>> from torchmetrics.classification import MulticlassHammingDistance >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.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 = 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], ... ]) >>> 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 = 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]]]) >>> 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]]) Fr rrrcCs(|\}}}}t|||||j|jdS)Nrrr rrrrrrrsz!MulticlassHammingDistance.computeNrrrrrr&h  `  r&c@r ) MultilabelHammingDistanceafComputes 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. Addtionally, 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)`` Args: 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. Example (preds is int tensor): >>> from torchmetrics.classification import MultilabelHammingDistance >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = 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]], ... ] ... ) >>> 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]]) Fr rrrc Cs*|\}}}}t|||||j|jddS)NT)rr multilabelr'rrrrr7sz!MultilabelHammingDistance.computeNrrrrrr)r(r)c@s|eZdZdZ        dded d ed eed eed eeddeeddeedeedede de fddZ dS)HammingDistancea(Computes 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 :mod:`BinaryHammingDistance`, :mod:`MulticlassHammingDistance` and :mod:`MultilabelHammingDistance` for the specific details of each argument influence and examples. Legacy Example: >>> target = torch.tensor([[0, 1], [1, 1]]) >>> preds = torch.tensor([[0, 1], [0, 1]]) >>> hamming_distance = HammingDistance(task="multilabel", num_labels=2) >>> hamming_distance(preds, target) tensor(0.2500) ?NmicroglobalTtask)r multiclassr* threshold num_classes num_labelsr)r-macroweightednoner)r. samplewisetop_k ignore_index validate_argskwargsrc Ks|dusJ| t||| d|dkrt|fi| S|dkr8t|ts'Jt|ts.Jt|||fi| S|dkrMt|tsCJt|||fi| Std|)N)rr:r;rr1r*z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )updatedictr isinstanceintr&r) ValueError) clsr0r2r3r4rrr9r:r;r<rrr__new__Us zHammingDistance.__new__)r,NNr-r.r/NT) r r!r"r#rfloatrr@r$rr rCrrrrr+>sB      r+)typingrrtorchrtyping_extensionsr'torchmetrics.classification.stat_scoresrrr.torchmetrics.functional.classification.hammingr torchmetrics.metricr r r&r)r+rrrrs     Ojl