a  de- @sHddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZddlmZdeeededd d Zdeeeeeddd d dZdeeeeedeeeedddZd eeeeedddddZd!eeeeedeeeedddZd"eeedeeeeedeeeed ddZdS)#)OptionalN)Tensor)Literal)'_binary_confusion_matrix_arg_validation_binary_confusion_matrix_format*_binary_confusion_matrix_tensor_validation_binary_confusion_matrix_update+_multiclass_confusion_matrix_arg_validation#_multiclass_confusion_matrix_format._multiclass_confusion_matrix_tensor_validation#_multiclass_confusion_matrix_update)ClassificationTaskNoMultilabel)linear quadraticnone)confmatweightsreturncCs|s|n|}|jd}|jddd}|jddd}|||}|dusZ|dkrt|}d|dd|d<|||}nf|dvrt|}|tj ||j |j d7}|d krt ||j nt||j d }ntd |d t||t||}d|S) zdReduce an un-normalized confusion matrix of shape (n_classes, n_classes) into the cohen kappa score.rT)ZdimZkeepdimNr)rr)dtypedevicerg@z Received zL for argument ``weights`` but should be either None, 'linear' or 'quadratic')Zis_floating_pointfloatshapesumtorchZ ones_likeflattenZreshapeZ zeros_likeZarangerrabsTpow ValueError)rrZ n_classesZsum0Zsum1expectedZw_matkr"{/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/classification/cohen_kappa.py_cohen_kappa_reduce!s$  , r$?) threshold ignore_indexrrcCs4t||ddd}||vr0td|d|ddS)zValidate non tensor input. - ``threshold`` has to be a float in the [0,1] range - ``ignore_index`` has to be None or int - ``weights`` has to be "linear" | "quadratic" | "none" | None N normalizerrrN(Expected argument `weight` to be one of , but got .)rr)r&r'rallowed_weightsr"r"r#"_binary_cohen_kappa_arg_validation9s r/T)predstargetr&rr' validate_argsrcCsB|rt|||t|||t||||\}}t||}t||S)aCalculate `Cohen's kappa score`_ that measures inter-annotator agreement for binary tasks. .. 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. 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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, ...)`` Additional dimension ``...`` will be flattened into the batch dimension. Args: preds: Tensor with predictions target: Tensor with true labels threshold: Threshold for transforming probability to binary (0,1) predictions weights: Weighting type to calculate the score. Choose from: - ``None`` or ``'none'``: no weighting - ``'linear'``: linear weighting - ``'quadratic'``: quadratic weighting 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. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import binary_cohen_kappa >>> target = tensor([1, 1, 0, 0]) >>> preds = tensor([0, 1, 0, 0]) >>> binary_cohen_kappa(preds, target) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_cohen_kappa >>> target = tensor([1, 1, 0, 0]) >>> preds = tensor([0.35, 0.85, 0.48, 0.01]) >>> binary_cohen_kappa(preds, target) tensor(0.5000) )r/rrrr$)r0r1r&rr'r2rr"r"r#binary_cohen_kappaKs :   r3) num_classesr'rrcCs4t||ddd}||vr0td|d|ddS)zValidate non tensor input. - ``num_classes`` has to be a int larger than 1 - ``ignore_index`` has to be None or int - ``weights`` has to be "linear" | "quadratic" | "none" | None Nr(r*r+r,r-)r r)r4r'rr.r"r"r#&_multiclass_cohen_kappa_arg_validations r5)r0r1r4rr'r2rcCsD|rt|||t||||t|||\}}t|||}t||S)a" Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for multiclass tasks. .. 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. 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, ...)`` Additional dimension ``...`` will be flattened into the batch dimension. Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifing the number of classes weights: Weighting type to calculate the score. Choose from: - ``None`` or ``'none'``: no weighting - ``'linear'``: linear weighting - ``'quadratic'``: quadratic weighting 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. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (pred is integer tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multiclass_cohen_kappa >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> multiclass_cohen_kappa(preds, target, num_classes=3) tensor(0.6364) Example (pred is float tensor): >>> from torchmetrics.functional.classification import multiclass_cohen_kappa >>> 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_cohen_kappa(preds, target, num_classes=3) tensor(0.6364) )r5r r r r$)r0r1r4rr'r2rr"r"r#multiclass_cohen_kappas ?  r6)binaryZ multiclass) r0r1taskr&r4rr'r2rcCsrt|}|tjkr&t||||||S|tjkr`t|tsNtdt|dt ||||||Std|dS)a7Calculate `Cohen's kappa score`_ that measures inter-annotator agreement. It is defined as. .. math:: \kappa = (p_o - p_e) / (1 - p_e) where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is the expected agreement when both annotators assign labels randomly. Note that :math:`p_e` is estimated using a per-annotator empirical prior over the class labels. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'`` or ``'multiclass'``. See the documentation of :func:`~torchmetrics.functional.classification.binary_cohen_kappa` and :func:`~torchmetrics.functional.classification.multiclass_cohen_kappa` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([1, 1, 0, 0]) >>> preds = tensor([0, 1, 0, 0]) >>> cohen_kappa(preds, target, task="multiclass", num_classes=2) tensor(0.5000) z+`num_classes` is expected to be `int` but `z was passed.`zNot handled value: N) r Zfrom_strBINARYr3Z MULTICLASS isinstanceintrtyper6)r0r1r8r&r4rr'r2r"r"r# cohen_kappas"    r=)N)r%NN)r%NNT)NN)NNT)r%NNNT)typingrrrZtyping_extensionsrZ7torchmetrics.functional.classification.confusion_matrixrrrrr r r r Ztorchmetrics.utilities.enumsr r$rr;r/boolr3r5r6r=r"r"r"r#s   (     D    K