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# Tweedie Deviance Score¶

## Module Interface¶

class torchmetrics.TweedieDevianceScore(power=0.0, compute_on_step=None, **kwargs)[source]

Computes the Tweedie Deviance Score between targets and predictions:

where is a tensor of targets values, and is a tensor of predictions.

Forward accepts

• preds (float tensor): (N,...)

• targets (float tensor): (N,...)

Parameters
• power (float) –

• 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.)

• compute_on_step (Optional[bool]) –

Forward only calls update() and returns None if this is set to False.

Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.

• kwargs (Dict[str, 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

Tensor

update(preds, targets)[source]

Update metric states with predictions and targets.

Parameters
Return type

None

## 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, and is a tensor of predictions.

Parameters
• preds (Tensor) – Predicted tensor with shape (N,...)

• targets (Tensor) – Ground truth tensor with shape (N,...)

• power (float) –

• 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

Tensor

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