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Tweedie Deviance Score

Module Interface

class torchmetrics.TweedieDevianceScore(power=0.0, **kwargs)[source]

Computes the Tweedie Deviance Score between targets and predictions:

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 y is a tensor of targets values, \hat{y} is a tensor of predictions, and p is the power.

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.)

  • 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

Tensor

update(preds, targets)[source]

Update metric states with predictions and targets.

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

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

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:

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 y is a tensor of targets values, \hat{y} is a tensor of predictions, and p is the power.

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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