Metric

class pytorch_forecasting.metrics.Metric(name: Optional[str] = None, quantiles: Optional[List[float]] = None, reduction='mean')

Bases: pytorch_lightning.metrics.metric.Metric

Base metric class that has basic functions that can handle predicting quantiles and operate in log space. See the Lightning documentation for details of how to implement a new metric

Other metrics should inherit from this base class

Initialize metric

Parameters

• name (str) – metric name. Defaults to class name.

• quantiles (List[float], optional) – quantiles for probability range. Defaults to None.

• reduction (str, optional) – Reduction, “none”, “mean” or “sqrt-mean”. Defaults to “mean”.

Methods

compute()	Abstract method that calcualtes metric
to_prediction(y_pred)	Convert network prediction into a point prediction.
to_quantiles(y_pred)	Convert network prediction into a quantile prediction.
update(y_actual)	Override this method to update the state variables of your metric class.

compute() → torch.Tensor

Abstract method that calcualtes metric

Should be overriden in derived classes

Parameters

• y_pred – network output

• y_actual – actual values

Returns

metric value on which backpropagation can be applied

Return type

torch.Tensor

to_prediction(y_pred: torch.Tensor) → torch.Tensor

Convert network prediction into a point prediction.

Parameters

y_pred – prediction output of network

Returns

point prediction

Return type

torch.Tensor

to_quantiles(y_pred: torch.Tensor) → torch.Tensor

Convert network prediction into a quantile prediction.

Parameters

y_pred – prediction output of network

Returns

prediction quantiles

Return type

torch.Tensor

update(y_actual: torch.Tensor)

Override this method to update the state variables of your metric class.

MASE MultiHorizonMetric