aixd.sampler.sampler_definitions.sampler_bayesian_kde

aixd.sampler.sampler_definitions.sampler_bayesian_kde(dobjects: List[DataObject], engine: SamplingEngine, objective: Operator, condition: Operator | None = None, data: DataFrame | array | None = None, callbacks_class: SamplingCallback | None = None) → SamplesGenerator[source]

A KDE fitted to some data, and then sampling optimizing for some objective. This process is slower than a KDE sampler with a condition.

Parameters:

  • dobjects (List[DataObject]) – List of DataObjects to sample from

  • engine (SamplingEngine) – To use for sampling

  • objective (Operator) – Objective operator to measure the performance of the obtained samples, in order to update the strategy of the sampler. Used in the Bayesian case.

  • condition (Operator) – The condition the samples need to satisfy

  • data (Union[pd.DataFrame, np.array]) – Just a quantile strategy for sampler that is fitted to some data, in order to provide samples that follow, in an univariate fashion, the distribution of the data.

  • callbacks_class (SamplingCallback) – In case we want to run some function on the samples obtained in the sampling process. This is intended for advanced usage.

Returns:

sampler (SamplesGenerator) – The sampler object

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