plot.visualization.plot_important_hyperparameter_contour

plot.visualization.plot_important_hyperparameter_contour(
    optimizer,
    max_imp=3,
    show=True,
    alpha=0.8,
    cmap='jet',
    num=100,
    add_points=True,
    grid_visible=True,
    contour_levels=30,
    figsize=(12, 10),
)

Plot surrogate contours for all combinations of the top max_imp important parameters.

This method identifies the most important parameters using importance scores, then generates surrogate contour plots for all pairwise combinations of these parameters. Factor (categorical) variables are handled by creating discrete grids and displaying factor level names on the axes.

Parameters

Name	Type	Description	Default
optimizer	`SpotOptimProtocol`	SpotOptim instance containing optimization data.	required
max_imp	int	Number of most important parameters to visualize. Defaults to 3. For max_imp=3, creates 3 plots: (0,1), (0,2), (1,2).	`3`
show	bool	If True, displays plots immediately. Defaults to True.	`True`
alpha	float	Transparency of 3D surface plots (0=transparent, 1=opaque). Defaults to 0.8.	`0.8`
cmap	str	Matplotlib colormap name. Defaults to ‘jet’.	`'jet'`
num	int	Number of grid points per dimension. Defaults to 100. For factor variables, uses the number of unique levels instead.	`100`
add_points	bool	If True, overlay evaluated points on contour plots. Defaults to True.	`True`
grid_visible	bool	If True, show grid lines. 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 max_imp is invalid.

Examples

import numpy as np
from spotoptim import SpotOptim
from spotoptim.plot.visualization import plot_important_hyperparameter_contour
from spotoptim.function import sphere
# Initialize and run optimizer with enough dimensions (here 4)
opt = SpotOptim(fun=sphere, bounds=[(-5, 5)]*4,
                 max_iter=10, n_initial=5,
                 var_name=['temp', 'pressure', 'velocity', 'acceleration'])
result = opt.optimize()
# Plot contours for top 3 important hyperparameters
plot_important_hyperparameter_contour(opt, max_imp=3, show=False)

Plotting surrogate contours for top 3 most important parameters:
  velocity: importance = 33.59% (type: float)
  acceleration: importance = 31.25% (type: float)
  pressure: importance = 21.46% (type: float)

Generating 3 surrogate plots...
  Plotting velocity vs acceleration
  Plotting velocity vs pressure
  Plotting acceleration vs pressure