skopt.plots.plot_gaussian_process

skopt.plots.plot_gaussian_process(res, **kwargs)

Plots the optimization results and the gaussian process for 1-D objective functions.

Parameters

resOptimizeResult

The result for which to plot the gaussian process.

axAxes, optional

The matplotlib axes on which to draw the plot, or None to create a new one.

n_callsint, default: -1

Can be used to evaluate the model at call n_calls.

objectivefunc, default: None

Defines the true objective function. Must have one input parameter.

n_pointsint, default: 1000

Number of data points used to create the plots

noise_levelfloat, default: 0

Sets the estimated noise level

show_legendboolean, default: True

When True, a legend is plotted.

show_titleboolean, default: True

When True, a title containing the found minimum value is shown

show_acq_funcboolean, default: False

When True, the acquisition function is plotted

show_next_pointboolean, default: False

When True, the next evaluated point is plotted

show_observationsboolean, default: True

When True, observations are plotted as dots.

show_muboolean, default: True

When True, the predicted model is shown.

Returns

axAxes

The matplotlib axes.

Examples using skopt.plots.plot_gaussian_process

Async optimization Loop

Bayesian optimization with skopt

Exploration vs exploitation

Use different base estimators for optimization