a  d@sdddlZddlmZddlmZdeededddZeed d d Zdeededd d ZdS)N)Tensor)Literalcountsrprobs)ratingsmodereturncCs|dkr^|jdks|s"td|jdd}tjjj||jdd ddd}|j d d}n"|d kr|jdksx|rtd |S) zUpdates the counts for fleiss kappa metric. Args: ratings: ratings matrix mode: whether ratings are provided as counts or probabilities rzIf argument ``mode`` is 'probs', ratings must have 3 dimensions with the format [n_samples, n_categories, n_raters] and be floating point.Zdim)Z num_classesrrzIf argument ``mode`` is `counts`, ratings must have 2 dimensions with the format [n_samples, n_categories] and be none floating point.) ndimZis_floating_point ValueErrorZargmaxtorchnnZ functionalone_hotshapeZpermutesum)rrrru/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/nominal/fleiss_kappa.py_fleiss_kappa_updates "r)rr cCsx|jd}|d}|}|jdd||}|djdd|||d}|}|d}||d|dS)z{Computes fleiss kappa from counts matrix. Args: counts: counts matrix of shape [n_samples, n_categories] rr r r gh㈵>)rrmaxmean)rtotalZn_raterZ num_ratersZp_iZp_jZp_barZpe_barrrr_fleiss_kappa_compute,s    rcCs"|dvrtdt||}t|S)aCalculatees `Fleiss kappa`_ a statistical measure for inter agreement between raters. .. math:: \kappa = \frac{\bar{p} - \bar{p_e}}{1 - \bar{p_e}} where :math:`\bar{p}` is the mean of the agreement probability over all raters and :math:`\bar{p_e}` is the mean agreement probability over all raters if they were randomly assigned. If the raters are in complete agreement then the score 1 is returned, if there is no agreement among the raters (other than what would be expected by chance) then a score smaller than 0 is returned. Args: ratings: Ratings of shape [n_samples, n_categories] or [n_samples, n_categories, n_raters] depedenent on `mode`. If `mode` is `counts`, `ratings` must be integer and contain the number of raters that chose each category. If `mode` is `probs`, `ratings` must be floating point and contain the probability/logits that each rater chose each category. mode: Whether `ratings` will be provided as counts or probabilities. Example: >>> # Ratings are provided as counts >>> import torch >>> from torchmetrics.functional.nominal import fleiss_kappa >>> _ = torch.manual_seed(42) >>> ratings = torch.randint(0, 10, size=(100, 5)).long() # 100 samples, 5 categories, 10 raters >>> fleiss_kappa(ratings) tensor(0.0089) Example: >>> # Ratings are provided as probabilities >>> import torch >>> from torchmetrics.functional.nominal import fleiss_kappa >>> _ = torch.manual_seed(42) >>> ratings = torch.randn(100, 5, 10).softmax(dim=1) # 100 samples, 5 categories, 10 raters >>> fleiss_kappa(ratings, mode='probs') tensor(-0.0105) rz5Argument ``mode`` must be one of ['counts', 'probs'].)rrr)rrrrrr fleiss_kappa>s% r)r)r)rrZtyping_extensionsrrrrrrrrs