MultivariateDistributionLoss#

class pytorch_forecasting.metrics.base_metrics.MultivariateDistributionLoss(name: Optional[str] = None, quantiles: List[float] = [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98], reduction='mean')[source]#

Bases: DistributionLoss

Base class for multivariate distribution losses.

Class should be inherited for all multivariate distribution losses, i.e. if a batch of values is predicted in one go and the batch dimension is not independent, but the time dimension still remains independent.

Initialize metric

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

  • quantiles (List[float], optional) – quantiles for probability range. Defaults to [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98].

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

Methods

loss(y_pred, y_actual)

Calculate negative likelihood

sample(y_pred, n_samples)

Sample from distribution.

loss(y_pred: Tensor, y_actual: Tensor) Tensor[source]#

Calculate negative likelihood

Parameters
  • y_pred – network output

  • y_actual – actual values

Returns

metric value on which backpropagation can be applied

Return type

torch.Tensor

sample(y_pred, n_samples: int) Tensor[source]#

Sample from distribution.

Parameters
  • y_pred – prediction output of network (shape batch_size x n_timesteps x n_paramters)

  • n_samples (int) – number of samples to draw

Returns

tensor with samples (shape batch_size x n_timesteps x n_samples)

Return type

torch.Tensor