plot.visualization.plot_progress(
optimizer,
show=True,
log_y=False,
figsize=(10, 6),
ylabel='Objective Value',
mo=False,
)Plot optimization progress showing all evaluations and best-so-far curve.
This method visualizes the optimization history, displaying both individual function evaluations and the cumulative best value found. Initial design points are shown as individual scatter points with a light grey background region, while sequential optimization iterations are connected with lines.
| Name | Type | Description | Default |
|---|---|---|---|
| optimizer | SpotOptimProtocol |
SpotOptim instance containing optimization data. | required |
| show | bool | Whether to display the plot. Defaults to True. | True |
| log_y | bool | Whether to use log scale for y-axis. Defaults to False. | False |
| figsize | tuple | Figure size as (width, height). Defaults to (10, 6). | (10, 6) |
| ylabel | str | Label for y-axis. Defaults to “Objective Value”. | 'Objective Value' |
| mo | bool | Whether to plot individual objectives if available. Defaults to False. | False |
| Name | Type | Description |
|---|---|---|
| ValueError | If optimization hasn’t been run yet. | |
| ImportError | If matplotlib is not installed. |
import numpy as np
from spotoptim import SpotOptim
from spotoptim.plot.visualization import plot_progress
from spotoptim.function import sphere
opt = SpotOptim(fun=sphere, bounds=[(-5, 5)]*2,
max_iter=10, n_initial=5, seed=42)
result = opt.optimize()
# Plot optimization progress (linear scale)
plot_progress(opt, log_y=False, show=False)
# Plot with log scale
plot_progress(opt, log_y=True, show=False)
