bayesvalidrox.surrogate_models.exploration.Exploration¶

class bayesvalidrox.surrogate_models.exploration.Exploration(ExpDesign, n_candidate, mc_criterion='mc-intersite-proj-th')¶

Bases: object

Created based on the Surrogate Modeling Toolbox (SUMO) [1].

[1] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K.,: 2010. A surrogate modeling and adaptive sampling toolbox for computer based design. Journal of machine learning research.-Cambridge, Mass., 11, pp.2051-2055. sumo@sumo.intec.ugent.be - http://sumo.intec.ugent.be

Attributes¶

ExpDesignobj: ExpDesign object.
n_candidateint: Number of candidate samples.
mc_criterionstr: Selection crieterion. The default is ‘mc-intersite-proj-th’. Another option is ‘mc-intersite-proj’.
wint: Number of random points in the domain for each sample of the training set.

__init__(ExpDesign, n_candidate, mc_criterion='mc-intersite-proj-th')¶

Methods

`__init__`(ExpDesign, n_candidate[, mc_criterion])
`approximate_voronoi`(w, samples)	An approximate (monte carlo) version of Matlab's voronoi command.
`get_exploration_samples`()	This function generates candidates to be selected as new design and their associated exploration scores.
`get_mc_samples`([all_candidates])	This function generates random samples based on Global Monte Carlo methods and their corresponding scores, based on [1].
`get_vornoi_samples`()	This function generates samples based on voronoi cells and their corresponding scores

approximate_voronoi(w, samples)¶

An approximate (monte carlo) version of Matlab’s voronoi command.

Arguments¶

samplesarray: Old experimental design to be used as center points for voronoi cells.

Returns¶

areasarray: An approximation of the voronoi cells’ areas.
all_candidates: list of arrays: A list of samples in each voronoi cell.

get_exploration_samples()¶

This function generates candidates to be selected as new design and their associated exploration scores.

Returns¶

all_candidatesarray of shape (n_candidate, n_params): A list of samples.
exploration_scores: arrays of shape (n_candidate): Exploration scores.

get_mc_samples(all_candidates=None)¶

This function generates random samples based on Global Monte Carlo methods and their corresponding scores, based on [1].

[1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient

space-filling and non-collapsing sequential design strategies for simulation-based modeling. European Journal of Operational Research , 214(3), pp.683-696. DOI: https://doi.org/10.1016/j.ejor.2011.05.032

Implemented methods to compute scores:

mc-intersite-proj
mc-intersite-proj-th

Arguments¶

all_candidatesarray, optional: Samples to compute the scores for. The default is None. In this case, samples will be generated by defined model input marginals.

Returns¶

new_samplesarray of shape (n_candidate, n_params): A list of samples.
exploration_scores: arrays of shape (n_candidate): Exploration scores.

get_vornoi_samples()¶

This function generates samples based on voronoi cells and their corresponding scores

Returns¶

new_samplesarray of shape (n_candidate, n_params): A list of samples.
exploration_scores: arrays of shape (n_candidate): Exploration scores.