LHCSamplingEngine

class aixd.sampler.engines.LHCSamplingEngine(features: List[str], scramble: bool = True, optimization: str | None = None, seed: int = 1730710435, **kwargs)[source]

Bases: AgnosticSamplingEngine

Samples in [0, 1] according to a Latin Hypercube sequence.

Parameters:

  • features (List[str]) – Names of features in the samples that should be produced

  • scramble (bool, optional, default=True) – If True, use LMS+shift scrambling. Otherwise, no scrambling is done

  • optimization (str, optional, default=None) – Whether to use an optimization scheme to improve the quality after sampling. Options are random-cd and lloyd. See https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.qmc.LatinHypercube.html

  • seed (int, optional, default=RANDOM_SEED_SAMPLING) – Seed value for reproducible results.

Methods

reset_states

Resets the engine to its initial state, where all calls to sample and update are forgotten.

sample

Performs the sampling.

Inherited Methods

deserialise

Allows to initialise a SamplingEngine by passing the identifier string along with with any specific arguments.

update

Updates the state of the sampling methods by providing the performance of the generated samples in the form of objectives and valid.
On this page