petitRADTRANS.sbi.inference

User-facing amortized inference services.

Classes

OODDiagnostic	Describe whether an observation is inside the training support.
AmortizedRetrievalResult	Return type for amortized inference queries.
AmortizedRetrieval	Serve trained SBI models through a retrieval-like interface.

Functions

_coerce_block_array(→ numpy.ndarray | None)
_robust_score_summary(→ tuple[float, float])

Module Contents

petitRADTRANS.sbi.inference._coerce_block_array(values: Any, target_size: int) → numpy.ndarray | None

petitRADTRANS.sbi.inference._robust_score_summary(values: numpy.ndarray, center: numpy.ndarray, scale: numpy.ndarray, valid_mask: numpy.ndarray) → tuple[float, float]

class petitRADTRANS.sbi.inference.OODDiagnostic

Describe whether an observation is inside the training support.

Attributes

score:

Scalar out-of-distribution score.

threshold:

Optional threshold used to convert the score into a pass/fail decision.

passed:

Optional boolean indicating whether the observation passed the OOD test.

metadata:

Auxiliary diagnostic metadata.

score: float

threshold: float | None = None

passed: bool | None = None

metadata: Mapping[str, Any]

class petitRADTRANS.sbi.inference.AmortizedRetrievalResult

Return type for amortized inference queries.

Attributes

posterior:

Posterior samples and any attached diagnostics.

posterior_predictive:

Optional posterior-predictive report for the same observation.

ood_diagnostic:

Optional in/out-of-distribution assessment.

metadata:

Additional metadata such as preprocessing usage during inference.

posterior: petitRADTRANS.sbi.posterior.PosteriorSamples

posterior_predictive: Any = None

ood_diagnostic: OODDiagnostic | None = None

metadata: Mapping[str, Any]

class petitRADTRANS.sbi.inference.AmortizedRetrieval(task: petitRADTRANS.sbi.task.SBITask, posterior_estimator: petitRADTRANS.sbi.posterior.PosteriorEstimator, simulator: petitRADTRANS.sbi.simulator.RuntimeSimulator | None = None, preprocessing_metadata: petitRADTRANS.sbi.preprocessing.TaskPreprocessingMetadata | Mapping[str, Any] | None = None)

Serve trained SBI models through a retrieval-like interface.

Parameters

task:

SBI task compatible with the trained posterior.

posterior_estimator:

Trained posterior estimator used for encoding and sampling.

simulator:

Optional simulator override used for posterior-predictive generation.

preprocessing_metadata:

Optional preprocessing metadata or payload. When omitted the inference service attempts to recover the preprocessing payload saved inside the posterior artifact.

Notes

Raw user-facing observation blocks are normalized internally before encoding when preprocessing metadata is available, but posterior-predictive comparisons remain on the original observation scale.

task

posterior_estimator

simulator = None

preprocessing_metadata

_resolve_preprocessing_metadata(preprocessing_metadata: petitRADTRANS.sbi.preprocessing.TaskPreprocessingMetadata | Mapping[str, Any] | None) → petitRADTRANS.sbi.preprocessing.TaskPreprocessingMetadata | None

prepare_observation_blocks(observation_blocks: list[petitRADTRANS.sbi.observation.ObservationBlock]) → list[petitRADTRANS.sbi.observation.ObservationBlock]

Normalize user-facing observation blocks when preprocessing metadata is available.

Parameters

observation_blocks:

Raw observation blocks provided by the caller.

Returns

list[ObservationBlock]

Either the original blocks or normalized copies, depending on whether preprocessing metadata is available.

infer(observation_blocks: list[petitRADTRANS.sbi.observation.ObservationBlock], n_posterior_samples: int = 1000, include_posterior_predictive: bool = False, posterior_predictive_interval_level: float = 0.9, n_predictive_forward_model_samples: int | None = None, seed: int | None = None) → AmortizedRetrievalResult

Infer a posterior for one structured observation.

Parameters

observation_blocks:

Raw observation blocks for one target system.

n_posterior_samples:

Number of posterior draws to generate.

include_posterior_predictive:

Whether to also generate a posterior-predictive summary.

posterior_predictive_interval_level:

Predictive interval level used when generating the optional posterior-predictive report.

n_predictive_forward_model_samples:

Number of posterior draws passed through the forward model when generating the posterior-predictive summary. When None all n_posterior_samples draws are forwarded. Set this to a small value (e.g. 50–200) to avoid a multi-hour hang when n_posterior_samples is large.

seed:

Optional seed for posterior and posterior-predictive sampling.

Returns

AmortizedRetrievalResult

Posterior samples and any optional predictive metadata.

posterior_predictive(posterior: petitRADTRANS.sbi.posterior.PosteriorSamples, observation_blocks: list[petitRADTRANS.sbi.observation.ObservationBlock], interval_level: float = 0.9, n_predictive_forward_model_samples: int | None = None, seed: int | None = None) → petitRADTRANS.sbi.calibration.PosteriorPredictiveReport

Generate posterior-predictive draws for a fitted observation.

Parameters

posterior:

Posterior samples associated with one observed system.

observation_blocks:

Original observation blocks used for user-facing comparison.

interval_level:

Central predictive interval level to report.

n_predictive_forward_model_samples:

Number of posterior draws passed through the forward model. When None all samples in posterior are used. Set this to a small value (e.g. 50–200) to keep the number of expensive petitRADTRANS calls manageable.

seed:

Optional seed for predictive simulation.

Returns

PosteriorPredictiveReport

Predictive summary for the supplied posterior samples.

diagnose_domain(observation_blocks: list[petitRADTRANS.sbi.observation.ObservationBlock]) → OODDiagnostic | None

Estimate whether the observation is in-distribution for the model.

Parameters

observation_blocks:

Observation blocks to diagnose.

Returns

OODDiagnostic | None

Robust support-distance diagnostic derived from preprocessing statistics, or None when preprocessing metadata is unavailable.