plot

Plot Bayesian optimization results

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Syntax

plot(results,'all')
plot(results,plotFcn1,plotFcn2,...)

Description

plot(results,'all') calls all predefined plot functions on results.

example

plot(results,plotFcn1,plotFcn2,...) calls the listed plot functions on results.

Examples

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Open Script

This example shows how to plot the error model and the best objective trace after the optimization has finished. The objective function for this example throws an error for points with norm larger than 2.

function f = makeanerror(x)
f = x.x1 - x.x2 - sqrt(4-x.x1^2-x.x2^2);


fun = @makeanerror;

Create the variables for optimization.

var1 = optimizableVariable('x1',[-5,5]);
var2 = optimizableVariable('x2',[-5,5]);
vars = [var1,var2];

Run the optimization without any plots. For reproducibility, set the random seed and use the 'expected-improvement-plus' acquisition function. Optimize for 60 iterations so the error model becomes well-trained.

rng default
results = bayesopt(fun,vars,'MaxObjectiveEvaluations',60,...
    'AcquisitionFunctionName','expected-improvement-plus',...
    'PlotFcn',[],'Verbose',0);

Plot the error model and the best objective trace.

plot(results,@plotConstraintModels,@plotMinObjective)

Input Arguments

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Bayesian optimization results, specified as a BayesianOptimization object.

Plot function, specified as a function handle.

There are several built-in plot functions:

Model Plots — Apply When D ≤ 2Description
@plotAcquisitionFunction

Plot the acquisition function surface.

@plotConstraintModels

Plot each constraint model surface. Negative values indicate feasible points.

Also plot a P(feasible) surface.

Also plot the error model, if it exists, which ranges from –1 to 1. Negative values mean that the model probably does not error, positive values mean that it probably does error. The model is:

Plotted error = 2*Probability(error) – 1.

@plotObjectiveEvaluationTimeModel

Plot the objective function evaluation time model surface.

@plotObjectiveModel

Plot the fun model surface, the estimated location of the minimum, and the location of the next proposed point to evaluate. For one-dimensional problems, plot envelopes one credible interval above and below the mean function, and envelopes one noise standard deviation above and below the mean.

Trace Plots — Apply to All DDescription
@plotObjective

Plot each observed function value versus the number of function evaluations.

@plotObjectiveEvaluationTime

Plot each observed function evaluation run time versus the number of function evaluations.

@plotMinObjective

Plot the minimum observed and estimated function values versus the number of function evaluations.

@plotElapsedTime

Plot three curves: the total elapsed time of the optimization, the total function evaluation time, and the total modeling and point selection time, all versus the number of function evaluations.

You can include a handle to your own plot functions. For details, see Bayesian Optimization Plot Functions.

Example: @plotObjective

Data Types: function_handle

Alternative Functionality

You can specify plot functions in the bayesopt PlotFcn name-value pair. This allows you to monitor the progress of the optimization.

See Also

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Introduced in R2016b

Statistics and Machine Learning Toolbox Documentation

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