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Bayesian Optimization Output Functions

What Is a Bayesian Optimization Output Function?

An output function is a function that is called at the end of every iteration of bayesopt. An output function can halt iterations. It can also create plots, save information to your workspace or to a file, or perform any other calculation you like.

Other than halting the iterations, output functions cannot change the course of a Bayesian optimization. They simply monitor the progress of the optimization.

Built-In Output Functions

These built-in output functions save your optimization results to a file or to the workspace.

  • @assignInBase — Saves your results after each iteration to a variable named 'BayesoptResults' in your workspace. To choose a different name, pass the SaveVariableName name-value pair.

  • @saveToFile — Saves your results after each iteration to a file named 'BayesoptResults.mat' in your current folder. To choose a different name or folder, pass the SaveFileName name-value pair.

For example, to save the results after each iteration to a workspace variable named 'BayesIterations',

results = bayesopt(fun,vars,'OutputFcn',@assignInBase, ...

Custom Output Functions

Write a custom output function with signature

stop = outputfun(results,state)

bayesopt passes the results and state variables to your function. Your function returns stop, which you set to true to halt the iterations, or to false to allow the iterations to continue.

results is an object of class BayesianOptimization. results contains the available information on the computations so far.

state has possible values:

  • 'initial'bayesopt is about to start iterating.

  • 'iteration'bayesopt just finished an iteration.

  • 'done'bayesopt just finished its final iteration.

For an example, see Bayesian Optimization Output Function.

Bayesian Optimization Output Function

This example shows how to use a custom output function with Bayesian optimization. The output function halts the optimization when the objective function, which is the cross-validation error rate, drops below 13%. The output function also plots the time for each iteration.

function stop = outputfun(results,state)
persistent h
stop = false;
switch state
    case 'initial'
        h = figure;
    case 'iteration'
        if results.MinObjective < 0.13
            stop = true;
        tms = results.IterationTimeTrace;
        xlabel('Iteration Number')
        ylabel('Time for Iteration')
        title('Time for Each Iteration')

The objective function is the cross validation loss of the KNN classification of the ionosphere data. Load the data and, for reproducibility, set the default random stream.

load ionosphere
rng default

Optimize over neighborhood size from 1 through 30, and for three distance metrics.

num = optimizableVariable('n',[1,30],'Type','integer');
dst = optimizableVariable('dst',{'chebychev','euclidean','minkowski'},'Type','categorical');
vars = [num,dst];

Set the cross-validation partition and objective function. For reproducibility, set the AcquisitionFunctionName to 'expected-improvement-plus'. Run the optimization.

c = cvpartition(351,'Kfold',5);
fun = @(x)kfoldLoss(fitcknn(X,Y,'CVPartition',c,'NumNeighbors',x.n,...
results = bayesopt(fun,vars,'OutputFcn',@outputfun,...
| Iter | Eval   | Objective   | Objective   | BestSoFar   | BestSoFar   |            n |          dst |
|      | result |             | runtime     | (observed)  | (estim.)    |              |              |
|    1 | Best   |     0.19943 |     0.31699 |     0.19943 |     0.19943 |           24 |    chebychev |
|    2 | Best   |     0.16809 |     0.19654 |     0.16809 |      0.1747 |            9 |    euclidean |
|    3 | Best   |     0.12536 |     0.16571 |     0.12536 |     0.12861 |            3 |    chebychev |

Optimization completed.
Total function evaluations: 3
Total elapsed time: 6.414 seconds
Total objective function evaluation time: 0.67924

Best observed feasible point:
    n       dst   
    _    _________

    3    chebychev

Observed objective function value = 0.12536
Estimated objective function value = 0.12861
Function evaluation time = 0.16571

Best estimated feasible point (according to models):
    n       dst   
    _    _________

    3    chebychev

Estimated objective function value = 0.12861
Estimated function evaluation time = 0.21764

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