ParametricDistributionScore#

class bayesflow.scores.ParametricDistributionScore(**kwargs)[source]#

Bases: ScoringRule

\(S(\hat p_\phi, \theta; k) = \log(\hat p_\phi(\theta))\)

Base class for scoring a predicted parametric probability distribution with the log-score of the probability of the realized value.

log_prob(*args, **kwargs)[source]#

sample(*args, **kwargs)[source]#

score(estimates: dict[str, Tensor], targets: Tensor, weights: Tensor = None) → Tensor[source]#

Computes the quantile-based scoring function.

This function extracts the “value” tensor from the estimates dictionary and computes the pointwise difference between the estimates and the targets, expanding the target dimensions as necessary.

The scoring function applies a quantile-based transformation to the difference, computing the mean score across a specified axis. The final score is then aggregated, optionally applying weights.

Parameters:

estimatesdict[str, Tensor]: A dictionary containing tensors of estimated values. The “value” key must be present.
targetsTensor: A tensor of true target values. The shape is adjusted to align with estimates.
weightsTensor, optional: A tensor of weights corresponding to each estimate-target pair. If provided, it is used to compute a weighted aggregate score.

Returns:

Tensor: The aggregated quantile-based score, computed using the absolute pointwise difference transformed by the quantile adjustment, optionally weighted.

aggregate(scores: Tensor, weights: Tensor = None) → Tensor#

Computes the mean of scores, optionally applying weights.

This function computes the mean value of the given scores. When weights are provided, it first multiplies the scores by the weights and then computes the mean of the result. If no weights are provided, it computes the mean of the scores.

Parameters:

scoresTensor: A tensor containing the scores to be aggregated.
weightsTensor, optional (default - None): A tensor of weights corresponding to each score. Must be the same shape as scores. If not provided, the function returns the mean of scores.

Returns:

Tensor: The aggregated score computed as a weighted mean if weights is provided, or as the simple mean of scores otherwise.

classmethod from_config(config)#

get_config()#

get_head(key: str, shape: tuple[int, ...]) → Sequential#

For a specified head key and shape, request corresponding head network.

Parameters:

keystr: Name of head for which to request a link.
shape: Shape: The necessary shape for the point estimators.

Returns:

headkeras.Sequential: Head network consisting of a learnable projection, a reshape and a link operation to parameterize estimates.

get_head_shapes_from_target_shape(target_shape: tuple[int, ...]) → dict[str, tuple[int, ...]]#: Request a dictionary of names and output shapes of required heads from the score.

get_link(key: str) → Layer#

For a specified key, request a link from network output to estimation target.

Parameters:

keystr: Name of head for which to request a link.

Returns:

linkkeras.Layer: Activation function linking network output to estimation target.

get_subnet(key: str) → Layer#

For a specified key, request a subnet to be used for projecting the shared condition embedding before reshaping to the heads output shape.

Parameters:

keystr: Name of head for which to request a link.

Returns:

linkkeras.Layer: Subnet projecting the shared condition embedding.