predictObjectiveEvaluationTime
Predict objective function run times at a set of points
Description
Examples
Predict Evaluation Time of Objective In an Optimized Model
This example shows how to estimate the objective function evaluation time in an optimized Bayesian model of SVM classification.
Create an optimized SVM model. For details of this model, see Optimize Cross-Validated Classifier Using bayesopt.
rng default grnpop = mvnrnd([1,0],eye(2),10); redpop = mvnrnd([0,1],eye(2),10); redpts = zeros(100,2); grnpts = redpts; for i = 1:100 grnpts(i,:) = mvnrnd(grnpop(randi(10),:),eye(2)*0.02); redpts(i,:) = mvnrnd(redpop(randi(10),:),eye(2)*0.02); end cdata = [grnpts;redpts]; grp = ones(200,1); grp(101:200) = -1; c = cvpartition(200,'KFold',10); sigma = optimizableVariable('sigma',[1e-5,1e5],'Transform','log'); box = optimizableVariable('box',[1e-5,1e5],'Transform','log'); minfn = @(z)kfoldLoss(fitcsvm(cdata,grp,'CVPartition',c,... 'KernelFunction','rbf','BoxConstraint',z.box,... 'KernelScale',z.sigma)); results = bayesopt(minfn,[sigma,box],'IsObjectiveDeterministic',true,... 'AcquisitionFunctionName','expected-improvement-plus','Verbose',0);
Predict the evaluation time for various points.
sigma = logspace(-5,5,11)'; box = 1e5*ones(size(sigma)); XTable = table(sigma,box); time = predictObjectiveEvaluationTime(results,XTable); [XTable,table(time)]
ans=11×3 table
sigma box time
______ _____ _______
1e-05 1e+05 0.16883
0.0001 1e+05 0.1644
0.001 1e+05 0.16016
0.01 1e+05 0.15117
0.1 1e+05 0.13905
1 1e+05 0.22858
10 1e+05 0.68226
100 1e+05 0.54217
1000 1e+05 0.22867
10000 1e+05 0.13816
1e+05 1e+05 0.14221
Input Arguments
results
— Bayesian optimization results
BayesianOptimization
object
Bayesian optimization results, specified as a BayesianOptimization
object.
XTable
— Prediction points
table with D columns
Prediction points, specified as a table with D columns, where D is the number of variables in the problem. The function performs its predictions on these points.
Data Types: table
Output Arguments
time
— Estimated objective evaluation times
N
-by-1
vector
Estimated objective evaluation times, returned as an
N
-by-1
vector, where
N
is the number of rows of
XTable
. The estimated values are the means of the
posterior distribution of the Gaussian process model of the evaluation times
of the objective function.
Version History
Introduced in R2016b
See Also
MATLAB-Befehl
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