vorob_dev

moocore.vorob_dev(data, /, sets, *, ref, ve=None)

Compute Vorob’ev deviation.

Parameters:

• data (ArrayLike) – Matrix of numerical values that represents multiple sets of points, where each row represents a point.

• sets (ArrayLike) – Vector that indicates the set of each point in data.

• ref (ArrayLike) – Reference point as a 1D vector. Must be same length as a single point in the data.

• ve (ArrayLike | None, default: None) – Vorob’ev expectation, e.g., as returned by vorob_t(). If not provided, it is calculated by calling vorob_t(data, sets, ref).

Returns:

float – Vorob’ev deviation.

See also

vorob_t

Compute Vorob’ev threshold and expectation.

Notes

For more background, see Binois et al.[1], Molchanov[2], Chevalier et al.[3].

References

[1]

Mickaël Binois, David Ginsbourger, and Olivier Roustant. Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. European Journal of Operational Research, 243(2):386–394, 2015. doi:10.1016/j.ejor.2014.07.032, [BibTeX].

[2]

Ilya Molchanov. Theory of Random Sets. Springer, 2005. [BibTeX].

[3]

Clément Chevalier, David Ginsbourger, Julien Bect, and Ilya Molchanov. Estimating and quantifying uncertainties on level sets using the Vorob’ev expectation and deviation with Gaussian process models. In Dariusz Ucinski, Anthony C. Atkinson, and Maciej Patan, editors, mODa 10–Advances in Model-Oriented Design and Analysis, pages 35–43. Springer International Publishing, Heidelberg, Germany, 2013. doi:10.1007/978-3-319-00218-7_5, [BibTeX].

Examples

>>> CPFs = moocore.get_dataset("CPFs.txt")
>>> vd = moocore.vorob_dev(CPFs[:, :-1], sets=CPFs[:, -1], ref=(2, 200))
>>> vd
3017.12989402326