MultiHorizonMetric

class pytorch_forecasting.metrics.MultiHorizonMetric(reduction: str = 'mean', **kwargs)

Bases: pytorch_forecasting.metrics.Metric

Abstract class for defining metric for a multihorizon forecast

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
loss(y_pred, target)	Calculate loss without reduction.
mask_losses(losses, lengths[, reduction])	Mask losses.
reduce_loss(losses, lengths[, reduction])	Reduce loss.
update(y_pred, target)	Update method of metric that handles masking of values.

compute()

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

loss(y_pred: Dict[str, torch.Tensor], target: torch.Tensor) → torch.Tensor

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

mask_losses(losses: torch.Tensor, lengths: torch.Tensor, reduction: Optional[str] = None) → torch.Tensor

Mask losses.

Parameters

• losses (torch.Tensor) – total loss. first dimenion are samples, second timesteps

• lengths (torch.Tensor) – total length

• reduction (str, optional) – type of reduction. Defaults to self.reduction.

Returns

masked losses

Return type

torch.Tensor

reduce_loss(losses: torch.Tensor, lengths: torch.Tensor, reduction: Optional[str] = None) → torch.Tensor

Reduce loss.

Parameters

• losses (torch.Tensor) – total loss. first dimenion are samples, second timesteps

• lengths (torch.Tensor) – total length

• reduction (str, optional) – type of reduction. Defaults to self.reduction.

Returns

reduced loss

Return type

torch.Tensor

update(y_pred, target)

Update method of metric that handles masking of values.

Do not override this method but loss() instead

Parameters

• y_pred (Dict[str, torch.Tensor]) – network output

• target (Union[torch.Tensor, rnn.PackedSequence]) – actual values

Returns

loss as a single number for backpropagation

Return type

torch.Tensor

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