CompositeMetric¶

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

Bases: torchmetrics.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, **kwargs)	Convert network prediction into a point prediction.
`to_quantiles`(y_pred, **kwargs)	Convert network prediction into a quantile prediction.
`update`(y_pred, y_actual)	Update composite metric

compute() → torch.Tensor[source]¶

Get metric

Returns: metric
Return type: torch.Tensor

to_prediction(y_pred: torch.Tensor, **kwargs) → torch.Tensor[source]¶

Convert network prediction into a point prediction.

Will use first metric in metrics attribute to calculate result.

Parameters

y_pred – prediction output of network
**kwargs – parameters to first metric to_prediction method

Returns

point prediction

Return type

torch.Tensor

to_quantiles(y_pred: torch.Tensor, **kwargs) → torch.Tensor[source]¶

Convert network prediction into a quantile prediction.

Will use first metric in metrics attribute to calculate result.

Parameters

y_pred – prediction output of network
**kwargs – parameters to first metric’s to_quantiles() method

Returns

prediction quantiles

Return type

torch.Tensor

update(y_pred: torch.Tensor, y_actual: torch.Tensor)[source]¶

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