NegativeBinomialDistributionLoss¶
- class pytorch_forecasting.metrics.NegativeBinomialDistributionLoss(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:
pytorch_forecasting.metrics.DistributionLossNegative binomial loss, e.g. for count data.
- Requirements for original target normalizer:
not centered normalization (only rescaled)
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
Map the a tensor of parameters to a probability distribution.
rescale_parameters(parameters, target_scale, ...)Rescale normalized parameters into the scale required for the output.
to_prediction(y_pred)Convert network prediction into a point prediction.
- distribution_class¶
alias of
torch.distributions.negative_binomial.NegativeBinomial
- map_x_to_distribution(x: torch.Tensor) torch.distributions.negative_binomial.NegativeBinomial[source]¶
Map the a tensor of parameters to a probability distribution.
- Parameters
x (torch.Tensor) – parameters for probability distribution. Last dimension will index the parameters
- Returns
- torch probability distribution as defined in the
class attribute
distribution_class
- Return type
distributions.Distribution
- rescale_parameters(parameters: torch.Tensor, target_scale: torch.Tensor, encoder: sklearn.base.BaseEstimator) torch.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
- to_prediction(y_pred: torch.Tensor) torch.Tensor[source]¶
Convert network prediction into a point prediction. In the case of this distribution prediction we need to derive the mean (as a point prediction) from the distribution parameters
- Parameters
y_pred – prediction output of network
binomial (in this case the two parameters for the negative) –
- Returns
mean prediction
- Return type
torch.Tensor