gluonts.torch.distributions.spliced_binned_pareto module

class gluonts.torch.distributions.spliced_binned_pareto.SplicedBinnedPareto(bins_lower_bound: float, bins_upper_bound: float, logits: torch.Tensor, upper_gp_xi: torch.Tensor, upper_gp_beta: torch.Tensor, lower_gp_xi: torch.Tensor, lower_gp_beta: torch.Tensor, numb_bins: int = 100, tail_percentile_gen_pareto: float = 0.05, validate_args=None)

Bases: gluonts.torch.distributions.binned_uniforms.BinnedUniforms

Spliced Binned-Pareto univariate distribution.

Parameters

• bins_lower_bound (The lower bound of the bin edges) –

• bins_upper_bound (The upper bound of the bin edges) –

• numb_bins (The number of equidistance bins to allocate between) – bins_lower_bound and bins_upper_bound. Default value is 100.

• tail_percentile_gen_pareto (The percentile of the distribution that is) – each tail. Default value is 0.05. NB: This symmetric percentile can still represent asymmetric upper and lower tails.

arg_constraints = {'logits': Real(), 'lower_gp_beta': GreaterThan(lower_bound=0.0), 'lower_gp_xi': GreaterThan(lower_bound=0.0), 'upper_gp_beta': GreaterThan(lower_bound=0.0), 'upper_gp_xi': GreaterThan(lower_bound=0.0)}

cdf(x: torch.Tensor)

Cumulative density tensor for a tensor of data points x.

‘x’ is expected to be of shape (*batch_shape)

has_rsample = False

log_prob(x: torch.Tensor, for_training=True)

Parameters

• x (a tensor of size 'batch_size', 1) –

• for_training (boolean to indicate a return of the log-probability, or) – of the loss (which is an adjusted log-probability)

pdf(x)

Probability for a tensor of data points x.

‘x’ is to have shape (*batch_shape)

support = Real()

class gluonts.torch.distributions.spliced_binned_pareto.SplicedBinnedParetoOutput(bins_lower_bound: float, bins_upper_bound: float, num_bins: int, tail_percentile_gen_pareto: float)

Bases: gluonts.torch.distributions.distribution_output.DistributionOutput

distr_cls

alias of gluonts.torch.distributions.spliced_binned_pareto.SplicedBinnedPareto

distribution(distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None) → gluonts.torch.distributions.binned_uniforms.BinnedUniforms

Construct the associated distribution, given the collection of constructor arguments and, optionally, a scale tensor.

Parameters

• distr_args – Constructor arguments for the underlying Distribution type.

• loc – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.

• scale – Optional tensor, of the same shape as the batch_shape+event_shape of the resulting distribution.

classmethod domain_map(logits: torch.Tensor, upper_gp_xi: torch.Tensor, upper_gp_beta: torch.Tensor, lower_gp_xi: torch.Tensor, lower_gp_beta: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]

Converts arguments to the right shape and domain.

The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape.

property event_shape: Tuple

Shape of each individual event compatible with the output object.