o yiI%@sddlmZmZddlZddlmZddlmZddlmZm Z ddl m Z m Z m Z ddlmZGdd d eZGd d d e ZGd d d ZdS))AnyOptionalN)Tensor)Literal)BinaryConfusionMatrixMulticlassConfusionMatrix)"_binary_cohen_kappa_arg_validation_cohen_kappa_reduce&_multiclass_cohen_kappa_arg_validation)Metricc seZdZUdZdZeed<dZeed<dZeed<    dd e d e e d e e d d ede ddf fdd ZdefddZZS)BinaryCohenKappaa Calculates `Cohen's kappa score`_ that measures inter-annotator agreement for binary tasks. It is defined as. .. math:: \kappa = (p_o - p_e) / (1 - p_e) where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is the expected agreement when both annotators assign labels randomly. Note that :math:`p_e` is estimated using a per-annotator empirical prior over the class labels. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A 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, ...)``. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``bck`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score Args: threshold: Threshold for transforming probability to binary (0,1) predictions ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation weights: Weighting type to calculate the score. Choose from: - ``None`` or ``'none'``: no weighting - ``'linear'``: linear weighting - ``'quadratic'``: quadratic weighting validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (preds is int tensor): >>> from torchmetrics.classification import BinaryCohenKappa >>> target = torch.tensor([1, 1, 0, 0]) >>> preds = torch.tensor([0, 1, 0, 0]) >>> metric = BinaryCohenKappa() >>> metric(preds, target) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryCohenKappa >>> target = torch.tensor([1, 1, 0, 0]) >>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01]) >>> metric = BinaryCohenKappa() >>> metric(preds, target) tensor(0.5000) Fis_differentiableThigher_is_betterfull_state_update?N threshold ignore_indexweightslinear quadraticnone validate_argskwargsreturnc <tj||fddd||rt|||||_||_dSNF) normalizer)super__init__rrr)selfrrrrr __class__[/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/cohen_kappa.pyrY   zBinaryCohenKappa.__init__cCt|j|jSNr confmatrr r#r#r$computegzBinaryCohenKappa.compute)rNNT)__name__ __module__ __qualname____doc__r bool__annotations__rrfloatrintrrrrr+ __classcell__r#r#r!r$r s.  7   r c seZdZUdZdZeed<dZeed<dZeed<   dde d e e d e e d d ed e ddf fdd Z defddZZS)MulticlassCohenKappaa Calculates `Cohen's kappa score`_ that measures inter-annotator agreement for multiclass tasks. It is defined as. .. math:: \kappa = (p_o - p_e) / (1 - p_e) where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is the expected agreement when both annotators assign labels randomly. Note that :math:`p_e` is estimated using a per-annotator empirical prior over the class labels. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Either 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, ...)``. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcck`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score Args: num_classes: Integer specifing the number of classes ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation weights: Weighting type to calculate the score. Choose from: - ``None`` or ``'none'``: no weighting - ``'linear'``: linear weighting - ``'quadratic'``: quadratic weighting validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (pred is integer tensor): >>> from torchmetrics.classification import MulticlassCohenKappa >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> metric = MulticlassCohenKappa(num_classes=3) >>> metric(preds, target) tensor(0.6364) Example (pred is float tensor): >>> from torchmetrics.classification import MulticlassCohenKappa >>> 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 = MulticlassCohenKappa(num_classes=3) >>> metric(preds, target) tensor(0.6364) Fr TrrN num_classesrrrrrrc rr)rrr rr)r r7rrrrr!r#r$rr%zMulticlassCohenKappa.__init__cCr&r'r(r*r#r#r$r+r,zMulticlassCohenKappa.compute)NNT)r-r.r/r0r r1r2rrr4rrrrrr+r5r#r#r!r$r6ks,  <   r6c@sZeZdZdZ     ddeddedeed eed d eed ed e de fddZ dS) CohenKappaaCalculates `Cohen's kappa score`_ that measures inter-annotator agreement. It is defined as. .. math:: \kappa = (p_o - p_e) / (1 - p_e) where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is the expected agreement when both annotators assign labels randomly. Note that :math:`p_e` is estimated using a per-annotator empirical prior over the class labels. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'`` or ``'multiclass'``. See the documentation of :mod:`BinaryCohenKappa` and :mod:`MulticlassCohenKappa` for the specific details of each argument influence and examples. Legacy Example: >>> target = torch.tensor([1, 1, 0, 0]) >>> preds = torch.tensor([0, 1, 0, 0]) >>> cohenkappa = CohenKappa(task="multiclass", num_classes=2) >>> cohenkappa(preds, target) tensor(0.5000) rNTtask)binary multiclassrr7rrrrrrcKs`|t|||d|dkrt|fi|S|dkr)t|ts!Jt|fi|Std|)N)rrrr:r;z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )updatedictr isinstancer4r6 ValueError)clsr9rr7rrrrr#r#r$__new__s zCohenKappa.__new__)rNNNT) r-r.r/r0rr3rr4r1rr rAr#r#r#r$r8s0  r8)typingrrtorchrtyping_extensionsrtorchmetrics.classificationrr2torchmetrics.functional.classification.cohen_kapparr r torchmetrics.metricr r r6r8r#r#r#r$s    NS