QuantileScore#

class bayesflow.scores.QuantileScore(q: Sequence[float] = None, links=None, **kwargs)[source]#

Bases: ScoringRule

\(S(\hat \theta_i, \theta; \tau_i) = (\hat \theta_i - \theta)(\mathbf{1}_{\hat \theta - \theta > 0} - \tau_i)\)

Scores predicted quantiles \(\hat \theta_i\) with the quantile score to match the quantile levels \(\hat \tau_i\).

get_config()[source]#

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

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

Scores a batch of probabilistic estimates of distributions based on samples of the corresponding distributions.

Parameters:

estimatesdict[str, Tensor]: Dictionary of estimates.
targetsTensor: Array of samples from the true distribution to evaluate the estimates.
weightsTensor: Array of weights for aggregating the scores.

Returns:

numeric_scoreTensor: Negatively oriented score evaluating the estimates, aggregated for the whole batch.

Examples

The following shows how to score estimates with a MeanScore. All ScoringRules follow this pattern, only differing in the structure of the estimates dictionary.

>>> import keras
>>> from bayesflow.scores import MeanScore
>>>
>>> # batch of samples from a normal distribution
>>> samples = keras.random.normal(shape=(100,))
>>>
>>> # batch of uninformed (random) estimates
>>> bad_estimates = {"value": keras.random.uniform((100,))}
>>>
>>> # batch of estimates that are closer to the true mean
>>> better_estimates = {"value": keras.random.normal(stddev=0.1, shape=(100,))}
>>>
>>> # calculate the score
>>> scoring_rule = MeanScore()
>>> scoring_rule.score(bad_estimates, samples)
<tf.Tensor: shape=(), dtype=float32, numpy=1.2243813276290894>
>>> scoring_rule.score(better_estimates, samples)
<tf.Tensor: shape=(), dtype=float32, numpy=1.013983130455017>

NOT_TRANSFORMING_LIKE_VECTOR_WARNING = ()#

This variable contains names of prediction heads that should lead to a warning when the adapter is applied in inverse direction to them.

Prediction heads can output estimates in spaces other than the target distribution space. To such estimates the adapter cannot be straightforwardly applied in inverse direction, because the adapter is built to map vectors from the inference variable space. When subclassing ScoringRule, add the names of such heads to the following list to warn users about difficulties with a type of estimate whenever the adapter is applied to them in inverse direction.

classmethod from_config(config)#

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

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

A head network has the following components that are called sequentially:

subnet: A keras.Layer.
dense: A trainable linear projection with as many units as are required by the next component.
reshape: Changes shape of output of projection to match requirements of next component.
link: Transforms unconstrained values into a constrained space for the final estimator. See links for examples.

This method initializes the components in reverse order to meet all requirements and returns them.

Parameters:

keystr: Name of head for which to request a link.
output_shape: Shape: The necessary shape of estimated values for the given key as returned by get_head_shapes_from_target_shape().

Returns:

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

get_link(key: str) → Layer#

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

If no link was specified for the key (e.g. upon initialization), return a linear activation.

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 further projection and reshaping to the heads output shape.

If no subnet was specified for the key (e.g. upon initialization), return just an instance of keras.layers.Identity.

Parameters:

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

Returns:

linkkeras.Layer: Subnet projecting the shared condition embedding.