TweedieLoss#

class pytorch_forecasting.metrics.point.TweedieLoss(reduction='mean', p: float = 1.5, **kwargs)[source]#

Bases: pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric

Tweedie loss

Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be tweedie-distributed.

Parameters

  • p (float, optional) – tweedie variance power which is greater equal 1.0 and smaller 2.0. Close to 2 shifts to Gamma distribution and close to 1 shifts to Poisson distribution. Defaults to 1.5.

  • reduction (str, optional) – How to reduce the loss. Defaults to “mean”.

Methods

loss(y_pred, y_true)

Calculate loss without reduction.

to_prediction(out)

Convert network prediction into a point prediction.
loss(y_pred, y_true)[source]#

Calculate loss without reduction. Override in derived classes

Parameters

  • y_pred – network output

  • y_actual – actual values

Returns

loss/metric as a single number for backpropagation

Return type

torch.Tensor

to_prediction(out: Dict[str, torch.Tensor])[source]#

Convert network prediction into a point prediction.

Parameters

y_pred – prediction output of network

Returns

point prediction

Return type

torch.Tensor

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