ImplicitQuantileNetworkDistributionLoss#

class pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss(quantiles: List[float] = [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98], input_size: Optional[int] = 16, hidden_size: Optional[int] = 32, n_loss_samples: Optional[int] = 64)[source]#

Bases: DistributionLoss

Implicit Quantile Network Distribution Loss.

Based on Probabilistic Time Series Forecasting with Implicit Quantile Networks. A network is used to directly map network outputs to a quantile.

Parameters
  • prediction_length (int) – maximum prediction length.

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

  • input_size (int, optional) – input size per prediction length. Defaults to 16.

  • hidden_size (int, optional) – hidden size per prediction length. Defaults to 64.

  • n_loss_samples (int, optional) – number of quantiles to sample to calculate loss.

Methods

loss(y_pred, y_actual)

Calculate negative likelihood

rescale_parameters(parameters, target_scale, ...)

Rescale normalized parameters into the scale required for the output.

sample(y_pred, n_samples)

Sample from distribution.

to_prediction(y_pred[, n_samples])

Convert network prediction into a point prediction.

to_quantiles(y_pred[, quantiles])

Convert network prediction into a quantile prediction.

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

rescale_parameters(parameters: Tensor, target_scale: Tensor, encoder: BaseEstimator) Tensor[source]#

Rescale normalized parameters into the scale required for the output.

Parameters
  • parameters (torch.Tensor) – normalized parameters (indexed by last dimension)

  • target_scale (torch.Tensor) – scale of parameters (n_batch_samples x (center, scale))

  • encoder (BaseEstimator) – original encoder that normalized the target in the first place

Returns

parameters in real/not normalized space

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

to_prediction(y_pred: Tensor, n_samples: int = 100) Tensor[source]#

Convert network prediction into a point prediction.

Parameters

y_pred – prediction output of network

Returns

mean prediction

Return type

torch.Tensor

to_quantiles(y_pred: Tensor, quantiles: Optional[List[float]] = None) Tensor[source]#

Convert network prediction into a quantile prediction.

Parameters
  • y_pred – prediction output of network

  • quantiles (List[float], optional) – quantiles for probability range. Defaults to quantiles as as defined in the class initialization.

Returns

prediction quantiles (last dimension)

Return type

torch.Tensor