pyuncertainnumber.propagation.helper¶

Classes¶

EpistemicDomain

Representation of the epistemic space which are indeed bounds of each dimension

Module Contents¶

class pyuncertainnumber.propagation.helper.EpistemicDomain(*vars: pyuncertainnumber.pba.intervals.Interval)¶

Representation of the epistemic space which are indeed bounds of each dimension

This class provides a set of handy functions to work with epistemic uncertainty in the form of bounds. It will be useful for tasks such as propagation or optimization where epistemic uncertainty is involved.

Parameters:: vars – a set of Interval variables

Tip

Recommended to use for optimisation tasks where the design bounds can be quickly specified with the toOptBounds() method.

See also

pyuncertainnumber.src.pyuncertainnumber.opt.bo : Bayesian optimisation class.

pyuncertainnumber.src.pyuncertainnumber.opt.ga : Genetic algorithm class.

Example

>>> from pyuncertainnumber import pba
>>> e = EpistemicDomain(pba.I(-1, 3), pba.I(5, 9))

>>> # convert the epistemic space to bounds for the optimizer
>>> e.toOptBounds(method='GA')  # `varbound` for genetic algorithm
>>> e.toOptBounds(method='BO')  # `xc_bounds` for Bayesian optimisation

>>> # perform lhs sampling on the epistemic space
>>> sample = e.lhs_sampling(1000)

lhs_sampling(n_samples: int)¶: perform lhs sampling on the epistemic space

lhs_plus_endpoints(n_samples: int)¶: perform lhs sampling on the epistemic space and add endpoints

bound_rep()¶: return the bounds (vec or matrix) of the epistemic space

toOptBounds(method: str)¶

convert the epistemic space to bounds for the optimizer

Parameters:: method (str) – the optimization method to use, e.g. ‘BayesOpt’, ‘GA’
Returns:: the bounds of the design varibale used for the optimisation method

to_GA_varBounds() → numpy.ndarray¶: convert the epistemic space to bounds for the genetic algorithm optimizer

to_BayesOptBounds(func_signature='vectorisation') → dict¶: convert the epistemic space to bounds for the Bayesian optimisation optimizer