tricands.tricands.tricands

tricands.tricands.tricands(
    X,
    p=0.5,
    fringe=True,
    nmax=None,
    best=None,
    ordering=None,
    vis=False,
    imgname='tricands.pdf',
    lower=0,
    upper=1,
)

Generate Triangulation Candidates for Bayesian Optimization. Assumes a bounding box of [lower, upper]^m.

Parameters

Name	Type	Description	Default
X	np.ndarray	Design matrix of shape (n_samples, n_features). Each row gives a design point and each column a feature.	required
p	float	Distance to the boundary for fringe candidates (0 = on hull, 1 = on boundary). Defaults to 0.5.	`0.5`
fringe	bool	Whether to include fringe points to allow exploration outside the convex hull. Defaults to True.	`True`
nmax	int	Maximum size of candidate set. If output exceeds this, strategic subsetting is employed. Defaults to 100 * n_features.	`None`
best	int	Index of the best (lowest) currently observed point. Used for strategic subsetting in Bayesian optimization. Defaults to None.	`None`
ordering	np.ndarray	Order of closeness of rows of X to a contour level. Used for contour location subsetting. Defaults to None.	`None`
vis	bool	Whether to visualize the triangulation. Only applicable to 2D designs. Defaults to False.	`False`
imgname	str	File name for saved plot if vis=True. Defaults to ‘tricands.pdf’.	`'tricands.pdf'`
lower	float	Lower bound of bounding box for all dimensions. Defaults to 0.	`0`
upper	float	Upper bound of bounding box for all dimensions. Defaults to 1.	`1`

Returns

Name	Type	Description
	np.ndarray	np.ndarray: Array of candidate points, shape (n_candidates, n_features).

Raises

Name	Type	Description
	Exception	If visualization is requested for non-2D data.
	Exception	If number of points is less than n_features + 1.
	Exception	If both ‘best’ and ‘ordering’ are provided.
	Exception	If X contains values outside [lower, upper].

Examples

>>> import numpy as np
>>> from spotoptim.tricands import tricands
>>> X = np.array([[0.1, 0.1], [0.9, 0.1], [0.5, 0.9], [0.2, 0.5]])
>>> candidates = tricands(X, fringe=True, p=0.5)
>>> print(candidates.shape)
(7, 2)