Tweedie Deviance Score¶
Module Interface¶
- class torchmetrics.TweedieDevianceScore(power=0.0, **kwargs)[source]
Computes the Tweedie Deviance Score between targets and predictions:
where is a tensor of targets values, is a tensor of predictions, and is the power.
Forward accepts
preds
(float tensor):(N,...)
targets
(float tensor):(N,...)
- Parameters
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¶ (
Any
) – Additional keyword arguments, see Advanced metric settings 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)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
- compute()[source]
Override this method to compute the final metric value from state variables synchronized across the distributed backend.
- Return type
Functional Interface¶
- torchmetrics.functional.tweedie_deviance_score(preds, targets, power=0.0)[source]
Computes the Tweedie Deviance Score between targets and predictions:
where is a tensor of targets values, is a tensor of predictions, and is the power.
- Parameters
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 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)
- Return type