MeanScore#

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

Bases: NormedDifferenceScore

\(S(\hat \theta, \theta) = | \hat \theta - \theta |^2\)

Scores a predicted mean with the squared error score.

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, ...])#: 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.

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

Computes the scoring function based on the absolute difference between estimates and targets.

This function extracts the “value” tensor from the estimates dictionary and computes the element-wise absolute difference between the estimates and the true targets. The difference is then exponentiated by self.k. The final score is computed using the aggregate method, which optionally applies weighting.

Parameters:

estimatesdict[str, Tensor]: A dictionary containing tensors of estimated values. The “value” key must be present.
targetsTensor: A tensor of true target values.
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 score based on the element-wise absolute difference raised to the power of self.k, optionally weighted.