aixd.sampler.sampler_definitions.sampler_conditional_kde

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

A KDE sampler fitted to some data, and also link to some conditions. When sampling, only the samples that satisfy some conditions are returned.

Parameters:

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

  • engine (SamplingEngine) – To use for sampling

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

  • data (Union[pd.DataFrame, np.array], optional, default=None) – 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, optional, default=None) – In case we want to run some function on the samples obtained in the sampling process. This is intended for advanced usage.

Returns:

SamplesGenerator – The sampler object