CompositeMetric

class pytorch_forecasting.metrics.CompositeMetric(metrics: List[pytorch_lightning.metrics.metric.Metric] = [], weights: Optional[List[float]] = None)

Bases: pytorch_lightning.metrics.metric.Metric

Metric that combines multiple metrics.

Metric does not have to be called explicitly but is automatically created when adding and multiplying metrics with each other.

Example

composite_metric = SMAPE() + 0.4 * MAE()

Parameters

• metrics (List[LightningMetric], optional) – list of metrics to combine. Defaults to [].

• weights (List[float], optional) – list of weights / multipliers for weights. Defaults to 1.0 for all metrics.

Methods

compute()	Get 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_pred, y_actual)	Update composite metric

compute() → torch.Tensor

Get metric

Returns

metric

Return type

torch.Tensor

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

Convert network prediction into a point prediction.

Will use first metric in metrics attribute to calculate result.

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.

Will use first metric in metrics attribute to calculate result.

Parameters

y_pred – prediction output of network

Returns

prediction quantiles

Return type

torch.Tensor

update(y_pred: torch.Tensor, y_actual: torch.Tensor)

Update composite metric

Parameters

• y_pred – network output

• y_actual – actual values

Returns

metric value on which backpropagation can be applied

Return type

torch.Tensor

BetaDistributionLoss CrossEntropy