petitRADTRANS.sbi.posterior_base

petitRADTRANS.sbi.posterior_base#

Posterior estimator interfaces and shared persistence base classes.

Attributes#

POSTERIOR_METADATA_SCHEMA_VERSION

Classes#

`TrainingArtifacts`	Structured outputs of a posterior training run.
`PosteriorSamples`	Posterior samples and optional per-sample diagnostics.
`PosteriorBatch`	Training batch passed to amortized posterior estimators.
`PosteriorEstimator`	Backend-agnostic interface for amortized posterior models.
`PersistentPosteriorEstimator`	Shared persistence helper for estimator backends with on-disk artifacts.

Module Contents#

petitRADTRANS.sbi.posterior_base.POSTERIOR_METADATA_SCHEMA_VERSION = '0.2.0'#

class petitRADTRANS.sbi.posterior_base.TrainingArtifacts#

Structured outputs of a posterior training run.

history: Mapping[str, Any]#

validation_metrics: Mapping[str, Any]#

metadata: Mapping[str, Any]#

class petitRADTRANS.sbi.posterior_base.PosteriorSamples#

Posterior samples and optional per-sample diagnostics.

samples: Any#

log_probabilities: Any = None#

weights: Any = None#

metadata: Mapping[str, Any]#

class petitRADTRANS.sbi.posterior_base.PosteriorBatch#

Training batch passed to amortized posterior estimators.

parameters: Any#

observations: Any#

metadata: Mapping[str, Any]#

class petitRADTRANS.sbi.posterior_base.PosteriorEstimator#

Bases: abc.ABC

Backend-agnostic interface for amortized posterior models.

abstractmethod fit(dataset: Any) → TrainingArtifacts#: Train the posterior estimator on a simulation dataset.

abstractmethod encode_observation(blocks: list[petitRADTRANS.sbi.observation.ObservationBlock]) → petitRADTRANS.sbi.observation.EncodedObservation#: Encode a structured observation into the estimator input space.

abstractmethod sample_posterior(observation: petitRADTRANS.sbi.observation.EncodedObservation, n_samples: int, seed: int | None = None) → PosteriorSamples#: Sample the amortized posterior for one encoded observation.

abstractmethod log_prob(observation: petitRADTRANS.sbi.observation.EncodedObservation, parameters: Any) → Any#: Evaluate posterior log-density when supported by the backend.

abstractmethod save(output_directory: str) → None#: Persist trained model weights and metadata.

classmethod load(input_directory: str) → PosteriorEstimator#

Abstractmethod:

Restore a saved estimator from disk.

class petitRADTRANS.sbi.posterior_base.PersistentPosteriorEstimator(parameter_dim: int, parameter_space: str = 'unconstrained', seed: int = 0, task_metadata: Mapping[str, Any] | None = None)#

Bases: PosteriorEstimator

Shared persistence helper for estimator backends with on-disk artifacts.

estimator_family = 'persistent_estimator'#

metadata_schema_version = '0.2.0'#

parameter_dim#

parameter_space = 'unconstrained'#

seed = 0#

task_metadata#

training_artifacts: TrainingArtifacts | None = None#

task_name: str | None#

task_version: str | None = None#

task_fingerprint: str | None = None#

observation_schema: Mapping[str, Any]#

preprocessing_metadata_payload: Mapping[str, Any]#

artifact_metadata: petitRADTRANS.sbi.artifacts.ArtifactMetadata | None = None#

abstractmethod _build_estimator_config() → dict[str, Any]#: Return backend-specific configuration for metadata persistence.

classmethod from_serialized_metadata(metadata: Mapping[str, Any]) → PersistentPosteriorEstimator#

Abstractmethod:

Rebuild an estimator instance from persisted metadata only.

abstractmethod save_backend_state(output_path: pathlib.Path) → None#: Persist backend-specific model state into the output directory.

abstractmethod load_backend_state(input_path: pathlib.Path) → None#: Restore backend-specific model state from the input directory.

static _load_training_artifacts(metadata: Mapping[str, Any]) → TrainingArtifacts | None#

hydrate_loaded_metadata(metadata: Mapping[str, Any]) → None#

_build_artifact_metadata_payload() → dict[str, Any]#

build_artifact_metadata(version: str) → petitRADTRANS.sbi.artifacts.ArtifactMetadata#: Assemble registry metadata for the currently trained estimator.

_build_serialized_metadata(artifact_metadata: petitRADTRANS.sbi.artifacts.ArtifactMetadata) → dict[str, Any]#

save(output_directory: str, artifact_registry: petitRADTRANS.sbi.artifacts.ArtifactRegistry | None = None, artifact_version: str = '0.1.0') → None#: Persist model weights, metadata, and optional artifact registration.

classmethod load(input_directory: str) → PersistentPosteriorEstimator#: Restore a saved persistent estimator from disk.