o .wi @sddlZddlmZddlmZddlmZddededed eeeffd d Zd ed ed efddZ ddededed efddZ dS)N)Tensor)_check_same_shape) _safe_xlogypredstargetspowerreturnc Cs2t||tj|j|jd}d|krdkr"nntd|d|dkr0tj||dd}n|dkrXt|dksBt|dkrJtd|d dt|||||}n|dkrt|dksjt|dkrrtd|d dt ||||d}n|dkrt|dkrtd|d n9d|krdkrnnt|dkst|dkrtd|d nt|dkst|dkrtd|d tt ||d|d|d|}|t|d|d|}t|d|d|}d|||}t |}tj t ||jd} || fS) aUpdate and returns variables required to compute Deviance Score for the given power. Check for same shape of input tensors. Args: preds: Predicted tensor targets: Ground truth tensor power: see :func:`tweedie_deviance_score` Example: >>> targets = torch.tensor([1.0, 2.0, 3.0, 4.0]) >>> preds = torch.tensor([4.0, 3.0, 2.0, 1.0]) >>> _tweedie_deviance_score_update(preds, targets, power=2) (tensor(4.8333), tensor(4)) )devicerz(Deviance Score is not defined for power=.)exponentz For power=zG, 'preds' has to be strictly positive and 'targets' cannot be negative.z:, both 'preds' and 'targets' have to be strictly positive.z&, 'preds' has to be strictly positive.zG, 'targets' has to be strictly positive and 'preds' cannot be negative.)rtorchzerosshaper ValueErrorpowanyrlogmaxsumtensornumel) rrr zero_tensordeviance_scoreterm_1term_2term_3sum_deviance_scorenum_observationsr!p/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/regression/tweedie_deviance.py_tweedie_deviance_score_updatesF    ( r#rr cCs||S)a Compute Deviance Score. Args: sum_deviance_score: Sum of deviance scores accumulated until now. num_observations: Number of observations encountered until now. Example: >>> targets = torch.tensor([1.0, 2.0, 3.0, 4.0]) >>> preds = torch.tensor([4.0, 3.0, 2.0, 1.0]) >>> sum_deviance_score, num_observations = _tweedie_deviance_score_update(preds, targets, power=2) >>> _tweedie_deviance_score_compute(sum_deviance_score, num_observations) tensor(1.2083) r!)rr r!r!r"_tweedie_deviance_score_computeVsr$cCst|||d\}}t||S)aCompute the `Tweedie Deviance Score`_. .. math:: deviance\_score(\hat{y},y) = \begin{cases} (\hat{y} - y)^2, & \text{for }p=0\\ 2 * (y * log(\frac{y}{\hat{y}}) + \hat{y} - y), & \text{for }p=1\\ 2 * (log(\frac{\hat{y}}{y}) + \frac{y}{\hat{y}} - 1), & \text{for }p=2\\ 2 * (\frac{(max(y,0))^{2 - p}}{(1 - p)(2 - p)} - \frac{y(\hat{y})^{1 - p}}{1 - p} + \frac{( \hat{y})^{2 - p}}{2 - p}), & \text{otherwise} \end{cases} where :math:`y` is a tensor of targets values, :math:`\hat{y}` is a tensor of predictions, and :math:`p` is the `power`. Args: preds: Predicted tensor with shape ``(N,...)`` targets: Ground truth tensor with shape ``(N,...)`` power: - `power < 0` : Extreme stable distribution. (Requires: preds > 0.) - `power = 0` : Normal distribution. (Requires: targets and preds can be any real numbers.) - `power = 1` : Poisson distribution. (Requires: targets >= 0 and y_pred > 0.) - `1 < p < 2` : Compound Poisson distribution. (Requires: targets >= 0 and preds > 0.) - `power = 2` : Gamma distribution. (Requires: targets > 0 and preds > 0.) - `power = 3` : Inverse Gaussian distribution. (Requires: targets > 0 and preds > 0.) - `otherwise` : Positive stable distribution. (Requires: targets > 0 and preds > 0.) Example: >>> from torchmetrics.functional.regression import tweedie_deviance_score >>> targets = torch.tensor([1.0, 2.0, 3.0, 4.0]) >>> preds = torch.tensor([4.0, 3.0, 2.0, 1.0]) >>> tweedie_deviance_score(preds, targets, power=2) tensor(1.2083) )r)r#r$)rrrrr r!r!r"tweedie_deviance_scorehs$ r%)r) rrtorchmetrics.utilities.checksrtorchmetrics.utilities.computerfloattupler#r$r%r!r!r!r"s   $@