plot.visualization.plot_surrogate

plot.visualization.plot_surrogate(
    optimizer,
    i=0,
    j=1,
    show=True,
    alpha=0.8,
    var_name=None,
    cmap='jet',
    num=100,
    vmin=None,
    vmax=None,
    add_points=True,
    grid_visible=True,
    contour_levels=30,
    figsize=(12, 10),
)

Plot the surrogate model for two dimensions.

Creates a 2x2 plot showing: - Top left: 3D surface of predictions - Top right: 3D surface of prediction uncertainty - Bottom left: Contour plot of predictions with evaluated points - Bottom right: Contour plot of prediction uncertainty

Parameters

Name	Type	Description	Default
optimizer	`SpotOptimProtocol`	SpotOptim instance containing optimization data and surrogate model.	required
i	int	Index of the first dimension to plot. Defaults to 0.	`0`
j	int	Index of the second dimension to plot. Defaults to 1.	`1`
show	bool	If True, displays the plot immediately. Defaults to True.	`True`
alpha	float	Transparency of the 3D surface plots (0=transparent, 1=opaque). Defaults to 0.8.	`0.8`
var_name	list of str	Names for each dimension. If None, uses instance var_name. Defaults to None.	`None`
cmap	str	Matplotlib colormap name. Defaults to ‘jet’.	`'jet'`
num	int	Number of grid points per dimension for mesh grid. Defaults to 100.	`100`
vmin	float	Minimum value for color scale. If None, determined from data. Defaults to None.	`None`
vmax	float	Maximum value for color scale. If None, determined from data. Defaults to None.	`None`
add_points	bool	If True, overlay evaluated points on contour plots. Defaults to True.	`True`
grid_visible	bool	If True, show grid lines on contour plots. Defaults to True.	`True`
contour_levels	int	Number of contour levels. Defaults to 30.	`30`
figsize	tuple of int	Figure size in inches (width, height). Defaults to (12, 10).	`(12, 10)`

Raises

Name	Type	Description
	ValueError	If optimization hasn’t been run yet, or if i, j are invalid.
	ImportError	If matplotlib is not installed.

Examples

import numpy as np
from spotoptim import SpotOptim
from spotoptim.plot.visualization import plot_surrogate
from spotoptim.function import sphere
opt = SpotOptim(fun=sphere,
                bounds=[(-5, 5), (-5, 5)],
                max_iter=10,
                n_initial=5,
                var_name=['x1', 'x2'])
result = opt.optimize()
# Plot surrogate model for dimensions 0 and 1
plot_surrogate(opt, i=0, j=1, show=False)