o yi5@sPddlmZddlZddlmZddlmZmZddlmZGdddeZ dS))AnyN)Tensor)_tweedie_deviance_score_compute_tweedie_deviance_score_update)MetriccseZdZUdZdZeed<dZdZeed<e ed<e ed< dd e d e d dffd d Z de de d dfddZ d e fddZZS)TweedieDevianceScorearComputes the `Tweedie Deviance Score`_ between targets and predictions: .. math:: deviance\_score(\hat{y},y) = \begin{cases} (\hat{y} - y)^2, & \text{for }p=0\\ 2 * (y * log(\frac{y}{\hat{y}}) + \hat{y} - y), & \text{for }p=1\\ 2 * (log(\frac{\hat{y}}{y}) + \frac{y}{\hat{y}} - 1), & \text{for }p=2\\ 2 * (\frac{(max(y,0))^{2 - p}}{(1 - p)(2 - p)} - \frac{y(\hat{y})^{1 - p}}{1 - p} + \frac{( \hat{y})^{2 - p}}{2 - p}), & \text{otherwise} \end{cases} where :math:`y` is a tensor of targets values, :math:`\hat{y}` is a tensor of predictions, and :math:`p` is the `power`. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predicted float tensor with shape ``(N,...)`` - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,...)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``deviance_score`` (:class:`~torch.Tensor`): A tensor with the deviance score Args: power: - power < 0 : Extreme stable distribution. (Requires: preds > 0.) - power = 0 : Normal distribution. (Requires: targets and preds can be any real numbers.) - power = 1 : Poisson distribution. (Requires: targets >= 0 and y_pred > 0.) - 1 < p < 2 : Compound Poisson distribution. (Requires: targets >= 0 and preds > 0.) - power = 2 : Gamma distribution. (Requires: targets > 0 and preds > 0.) - power = 3 : Inverse Gaussian distribution. (Requires: targets > 0 and preds > 0.) - otherwise : Positive stable distribution. (Requires: targets > 0 and preds > 0.) kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics import TweedieDevianceScore >>> targets = torch.tensor([1.0, 2.0, 3.0, 4.0]) >>> preds = torch.tensor([4.0, 3.0, 2.0, 1.0]) >>> deviance_score = TweedieDevianceScore(power=2) >>> deviance_score(preds, targets) tensor(1.2083) Tis_differentiableNFfull_state_updatesum_deviance_scorenum_observationspowerkwargsreturnc sptjd i|d|krdkrnntd|d||_|jdtddd|jd tddddS) Nrz(Deviance Score is not defined for power=.r r sum)dist_reduce_fxr )super__init__ ValueErrorr add_statetorchtensor)selfr r __class__r\/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/tweedie_deviance.pyrNs zTweedieDevianceScore.__init__predstargetscCs2t|||j\}}|j|7_|j|7_dS)z2Update metric states with predictions and targets.N)rr r r )rrr r r rrrupdate\szTweedieDevianceScore.updatecCst|j|jS)N)rr r )rrrrcomputecszTweedieDevianceScore.compute)r )__name__ __module__ __qualname____doc__rbool__annotations__higher_is_betterr rfloatrrr!r" __classcell__rrrrrs"  - r) typingrrr3torchmetrics.functional.regression.tweedie_deviancerrtorchmetrics.metricrrrrrrs