Байесова оптимизация с длинными массивами
Этот пример показывает, как использовать Байесовую оптимизацию, чтобы выбрать оптимальные параметры для обучения классификатор ядра при помощи аргумента пары "имя-значение" 'OptimizeHyperparameters'. airlinesmall.csv набора выборочных данных является большим набором данных, который содержит табличный файл данных о полете. Этот пример составляет длинную таблицу, содержащую данные, и использует длинную таблицу, чтобы запустить процедуру оптимизации.
Получите данные в MATLAB®
Создайте datastore, который ссылается на местоположение папки с данными. Данные могут содержаться в одном файле, наборе файлов или целой папке. Для папок, которые содержат набор файлов, можно задать целое местоположение папки или использовать подстановочный символ, '*.csv', чтобы включать несколько файлов с тем же расширением файла в datastore. Выберите подмножество переменных, чтобы работать с и обработать значения 'NA' как недостающие данные так, чтобы datastore заменил их на значения NaN. Составьте длинную таблицу, которая содержит данные в datastore.
ds = datastore('airlinesmall.csv'); ds.SelectedVariableNames = {'Month','DayofMonth','DayOfWeek',... 'DepTime','ArrDelay','Distance','DepDelay'}; ds.TreatAsMissing = 'NA'; tt = tall(ds) % Tall table
Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).
tt =
M×7 tall table
Month DayofMonth DayOfWeek DepTime ArrDelay Distance DepDelay
_____ __________ _________ _______ ________ ________ ________
10 21 3 642 8 308 12
10 26 1 1021 8 296 1
10 23 5 2055 21 480 20
10 23 5 1332 13 296 12
10 22 4 629 4 373 -1
10 28 3 1446 59 308 63
10 8 4 928 3 447 -2
10 10 6 859 11 954 -1
: : : : : : :
: : : : : : :
Когда вы выполняете вычисления на длинных массивах, среда выполнения по умолчанию использует или локальный сеанс работы с MATLAB или локальный параллельный пул (если у вас есть Parallel Computing Toolbox™). Можно использовать функцию mapreducer, чтобы изменить среду выполнения.
Подготовьте метки класса и данные о предикторе
Определите рейсы, которые являются поздними на 10 минут или больше путем определения логической переменной, которая верна для позднего рейса. Эта переменная содержит метки класса. Предварительный просмотр этой переменной включает первые несколько строк.
Y = tt.DepDelay > 10 % Class labelsY = M×1 tall logical array 1 0 1 1 0 1 0 0 : :
Создайте длинный массив для данных о предикторе.
X = tt{:,1:end-1} % Predictor dataX =
M×6 tall double matrix
Columns 1 through 5
10 21 3 642 8
10 26 1 1021 8
10 23 5 2055 21
10 23 5 1332 13
10 22 4 629 4
10 28 3 1446 59
10 8 4 928 3
10 10 6 859 11
: : : : :
: : : : :
Column 6
308
296
480
296
373
308
447
954
:
:
Удалите строки в X и Y, которые содержат недостающие данные.
R = rmmissing([X Y]); % Data with missing entries removed
X = R(:,1:end-1);
Y = R(:,end); Выполните байесовую оптимизацию Используя OptimizeHyperparameters
Оптимизируйте гиперпараметры автоматически с помощью аргумента пары "имя-значение" 'OptimizeHyperparameters'.
Стандартизируйте переменные прогноза.
Z = zscore(X);
Найдите оптимальные значения для аргументов пары "имя-значение" 'KernelScale' и 'Lambda', которые минимизируют пятикратную потерю перекрестной проверки. Для воспроизводимости используйте функцию приобретения 'expected-improvement-plus' и установите seed генераторов случайных чисел с помощью rng и tallrng. Результаты могут отличаться в зависимости от количества рабочих и среды выполнения для длинных массивов. Для получения дополнительной информации смотрите Управление Где Ваши Выполнения Кода (MATLAB).
rng('default') tallrng('default') Mdl = fitckernel(Z,Y,'Verbose',0,'OptimizeHyperparameters','auto',... 'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName','expected-improvement-plus'))
Evaluating tall expression using the Parallel Pool 'local': - Pass 1 of 2: Completed in 9 sec - Pass 2 of 2: Completed in 8.9 sec Evaluation completed in 18 sec
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.7 sec
Evaluation completed in 2.7 sec
|=====================================================================================================|
| Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | KernelScale | Lambda |
| | result | | runtime | (observed) | (estim.) | | |
|=====================================================================================================|
| 1 | Best | 0.19672 | 153.5 | 0.19672 | 0.19672 | 1.2297 | 0.0080902 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.5 sec
| 2 | Accept | 0.19672 | 69.775 | 0.19672 | 0.19672 | 0.039643 | 2.5756e-05 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.1 sec
| 3 | Accept | 0.19672 | 72.586 | 0.19672 | 0.19672 | 0.02562 | 1.2555e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.5 sec
| 4 | Accept | 0.19672 | 81.345 | 0.19672 | 0.19672 | 92.644 | 1.2056e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 5 | Best | 0.11469 | 116.54 | 0.11469 | 0.12698 | 11.173 | 0.00024836 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 6 | Best | 0.11365 | 108.21 | 0.11365 | 0.11373 | 10.609 | 0.00025761 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.5 sec
| 7 | Accept | 0.19672 | 66.926 | 0.11365 | 0.11373 | 0.0059498 | 0.00043861 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 8 | Accept | 0.12122 | 121.38 | 0.11365 | 0.11371 | 11.44 | 0.00045722 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.5 sec
| 9 | Best | 0.10417 | 55.569 | 0.10417 | 0.10417 | 8.0424 | 6.7998e-05 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.9 sec
Evaluation completed in 2 sec
| 10 | Accept | 0.10433 | 56.204 | 0.10417 | 0.10417 | 9.6694 | 1.4948e-05 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 11 | Best | 0.10409 | 56.93 | 0.10409 | 0.10411 | 6.2099 | 6.1093e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.4 sec
Evaluation completed in 1.5 sec
| 12 | Best | 0.10383 | 61.958 | 0.10383 | 0.10404 | 5.6767 | 7.6134e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.5 sec
| 13 | Accept | 0.10408 | 60.655 | 0.10383 | 0.10365 | 8.1769 | 8.5993e-09 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2.1 sec
| 14 | Accept | 0.10404 | 57.069 | 0.10383 | 0.10361 | 7.6191 | 6.4079e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.9 sec
Evaluation completed in 1.9 sec
| 15 | Best | 0.10351 | 56.101 | 0.10351 | 0.10362 | 4.2987 | 9.2645e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 16 | Accept | 0.10404 | 60.269 | 0.10351 | 0.10362 | 4.8747 | 1.7838e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 17 | Accept | 0.10657 | 119.73 | 0.10351 | 0.10357 | 4.8239 | 0.00016344 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.4 sec
Evaluation completed in 1.5 sec
| 18 | Best | 0.10299 | 55.899 | 0.10299 | 0.10358 | 3.5555 | 2.7165e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 19 | Accept | 0.10366 | 56.413 | 0.10299 | 0.10324 | 3.8035 | 1.3542e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 20 | Accept | 0.10337 | 55.97 | 0.10299 | 0.10323 | 3.806 | 1.8101e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.9 sec
Evaluation completed in 2 sec
|=====================================================================================================|
| Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | KernelScale | Lambda |
| | result | | runtime | (observed) | (estim.) | | |
|=====================================================================================================|
| 21 | Accept | 0.10345 | 56.155 | 0.10299 | 0.10322 | 3.3655 | 9.082e-09 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.5 sec
| 22 | Accept | 0.19672 | 82.67 | 0.10299 | 0.10322 | 999.62 | 1.2609e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2.1 sec
| 23 | Accept | 0.10315 | 56.062 | 0.10299 | 0.10306 | 3.6716 | 1.2445e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 24 | Accept | 0.19672 | 68.283 | 0.10299 | 0.10306 | 0.0010004 | 2.6214e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.9 sec
Evaluation completed in 2 sec
| 25 | Accept | 0.19672 | 67.775 | 0.10299 | 0.10306 | 0.21865 | 0.0026529 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2.1 sec
| 26 | Accept | 0.19672 | 84.329 | 0.10299 | 0.10306 | 299.92 | 0.0032109 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.4 sec
Evaluation completed in 1.4 sec
| 27 | Accept | 0.19672 | 67.871 | 0.10299 | 0.10306 | 0.002436 | 0.0040428 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.4 sec
Evaluation completed in 2.4 sec
| 28 | Accept | 0.19672 | 75.278 | 0.10299 | 0.10305 | 0.50559 | 3.3667e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 29 | Accept | 0.10354 | 60.527 | 0.10299 | 0.10313 | 3.7754 | 9.5626e-09 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2 sec
| 30 | Accept | 0.10405 | 59.206 | 0.10299 | 0.10315 | 8.9864 | 2.3136e-07 |
__________________________________________________________
Optimization completed.
MaxObjectiveEvaluations of 30 reached.
Total function evaluations: 30
Total elapsed time: 2243.1781 seconds.
Total objective function evaluation time: 2221.1816
Best observed feasible point:
KernelScale Lambda
___________ __________
3.5555 2.7165e-06
Observed objective function value = 0.10299
Estimated objective function value = 0.10315
Function evaluation time = 55.8993
Best estimated feasible point (according to models):
KernelScale Lambda
___________ __________
3.6716 1.2445e-08
Estimated objective function value = 0.10315
Estimated function evaluation time = 57.638
Mdl =
ClassificationKernel
PredictorNames: {'x1' 'x2' 'x3' 'x4' 'x5' 'x6'}
ResponseName: 'Y'
ClassNames: [0 1]
Learner: 'svm'
NumExpansionDimensions: 256
KernelScale: 3.6716
Lambda: 1.2445e-08
BoxConstraint: 665.9442
Properties, Methods
Выполните байесовую оптимизацию при помощи bayesopt
Также можно использовать функцию bayesopt, чтобы найти оптимальные значения гиперпараметров.
Разделите набор данных в наборы обучающих данных и наборы тестов. Задайте 1/3 выборку затяжки для набора тестов.
rng('default') % For reproducibility tallrng('default') % For reproducibility Partition = cvpartition(Y,'Holdout',1/3); trainingInds = training(Partition); % Indices for the training set testInds = test(Partition); % Indices for the test set
Извлеките данные об обучении и тестировании и стандартизируйте данные о предикторе.
Ytrain = Y(trainingInds); % Training class labels Xtrain = X(trainingInds,:); [Ztrain,mu,stddev] = zscore(Xtrain); % Standardized training data Ytest = Y(testInds); % Testing class labels Xtest = X(testInds,:); Ztest = (Xtest-mu)./stddev; % Standardized test data
Задайте переменные sigma и lambda, чтобы найти оптимальные значения для аргументов пары "имя-значение" 'KernelScale' и 'Lambda'. Используйте optimizableVariable и задайте широкий спектр для переменных, потому что оптимальные значения неизвестны. Примените логарифмическое преобразование к переменным, чтобы искать оптимальные значения на логарифмической шкале.
N = gather(numel(Ytrain)); % Evaluate the length of the tall training array in memoryEvaluating tall expression using the Parallel Pool 'local': - Pass 1 of 1: Completed in 1.2 sec Evaluation completed in 1.2 sec
sigma = optimizableVariable('sigma',[1e-3,1e3],'Transform','log'); lambda = optimizableVariable('lambda',[(1e-3)/N, (1e3)/N],'Transform','log');
Создайте целевую функцию для Байесовой оптимизации. Целевая функция берет в таблице, которая содержит переменные sigma и lambda, и затем вычисляет значение классификации потерь для бинарной Гауссовой модели классификации ядер, обученной с помощью функции fitckernel. Установите 'Verbose',0 в fitckernel подавлять итеративное отображение диагностической информации.
minfn = @(z)gather(loss(fitckernel(Ztrain,Ytrain, ... 'KernelScale',z.sigma,'Lambda',z.lambda,'Verbose',0), ... Ztest,Ytest));
Оптимизируйте параметры [sigma,lambda] модели классификации ядер относительно потери классификации при помощи bayesopt. По умолчанию bayesopt отображает итеративную информацию об оптимизации в командной строке. Для воспроизводимости, набор опция AcquisitionFunctionName к 'expected-improvement-plus'. Функция приобретения по умолчанию зависит от времени выполнения и, поэтому, может дать переменные результаты.
results = bayesopt(minfn,[sigma,lambda],'AcquisitionFunctionName','expected-improvement-plus')
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 2: Completed in 1 sec
- Pass 2 of 2: Completed in 1.9 sec
Evaluation completed in 3 sec
|=====================================================================================================|
| Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | sigma | lambda |
| | result | | runtime | (observed) | (estim.) | | |
|=====================================================================================================|
| 1 | Best | 0.19651 | 167.42 | 0.19651 | 0.19651 | 1.2297 | 0.012135 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 2 | Accept | 0.19651 | 230.16 | 0.19651 | 0.19651 | 0.039643 | 3.8633e-05 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.2 sec
Evaluation completed in 2.2 sec
| 3 | Accept | 0.19651 | 161.51 | 0.19651 | 0.19651 | 0.02562 | 1.8832e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.7 sec
Evaluation completed in 1.7 sec
| 4 | Accept | 0.19651 | 106.77 | 0.19651 | 0.19651 | 92.644 | 1.8084e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.1 sec
| 5 | Accept | 0.19651 | 106.9 | 0.19651 | 0.19651 | 978.95 | 0.00015066 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 6 | Accept | 0.19651 | 185.97 | 0.19651 | 0.19651 | 0.0089609 | 0.0059189 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.3 sec
Evaluation completed in 2.3 sec
| 7 | Accept | 0.19651 | 107.88 | 0.19651 | 0.19651 | 97.709 | 0.00010771 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 8 | Accept | 0.19651 | 97.478 | 0.19651 | 0.19651 | 422.03 | 4.841e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.2 sec
Evaluation completed in 2.2 sec
| 9 | Accept | 0.19651 | 167.96 | 0.19651 | 0.19651 | 0.0012826 | 5.5116e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.7 sec
| 10 | Accept | 0.19651 | 212.44 | 0.19651 | 0.19651 | 0.031682 | 3.1742e-05 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 11 | Best | 0.10029 | 65.889 | 0.10029 | 0.1003 | 3.9327 | 3.5022e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
| 12 | Accept | 0.10059 | 65.692 | 0.10029 | 0.1003 | 3.1844 | 7.385e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.2 sec
Evaluation completed in 2.2 sec
| 13 | Accept | 0.10098 | 69.683 | 0.10029 | 0.10031 | 5.2372 | 4.9773e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.6 sec
| 14 | Accept | 0.10133 | 72.595 | 0.10029 | 0.099825 | 4.2748 | 1.262e-06 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.7 sec
| 15 | Accept | 0.10141 | 76.94 | 0.10029 | 0.10059 | 3.3388 | 2.1269e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
| 16 | Accept | 0.12235 | 143.29 | 0.10029 | 0.10058 | 9.0019 | 0.0010713 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.2 sec
Evaluation completed in 2.2 sec
| 17 | Accept | 0.10668 | 141.51 | 0.10029 | 0.10042 | 3.6288 | 0.00063589 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.1 sec
| 18 | Best | 0.10016 | 70.404 | 0.10016 | 0.10058 | 4.311 | 1.2975e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.1 sec
| 19 | Accept | 0.10034 | 68.692 | 0.10016 | 0.10001 | 3.8228 | 8.1818e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 20 | Accept | 0.10123 | 71.019 | 0.10016 | 0.10004 | 6.3387 | 1.2575e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
|=====================================================================================================|
| Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | sigma | lambda |
| | result | | runtime | (observed) | (estim.) | | |
|=====================================================================================================|
| 21 | Accept | 0.10113 | 70.865 | 0.10016 | 0.099988 | 5.1223 | 1.2705e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.1 sec
Evaluation completed in 2.2 sec
| 22 | Accept | 0.10041 | 70.621 | 0.10016 | 0.10006 | 3.6363 | 4.4732e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
| 23 | Accept | 0.10061 | 64.482 | 0.10016 | 0.10019 | 3.7705 | 2.8022e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
| 24 | Accept | 0.10044 | 69.779 | 0.10016 | 0.10025 | 3.6538 | 3.4072e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2 sec
Evaluation completed in 2.1 sec
| 25 | Accept | 0.19651 | 220.15 | 0.10016 | 0.10026 | 0.24021 | 1.3156e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
| 26 | Best | 0.10016 | 69.161 | 0.10016 | 0.10024 | 3.5161 | 4.6627e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.7 sec
| 27 | Accept | 0.16207 | 59.461 | 0.10016 | 0.10024 | 28.573 | 1.356e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.5 sec
Evaluation completed in 1.6 sec
| 28 | Accept | 0.10036 | 70.388 | 0.10016 | 0.10025 | 3.5285 | 7.3662e-07 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1.6 sec
Evaluation completed in 1.6 sec
| 29 | Accept | 0.19651 | 166.35 | 0.10016 | 0.10025 | 0.0038154 | 1.3372e-08 |
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 2.2 sec
Evaluation completed in 2.2 sec
| 30 | Accept | 0.19651 | 180.6 | 0.10016 | 0.10024 | 0.12353 | 0.012337 |
__________________________________________________________
Optimization completed.
MaxObjectiveEvaluations of 30 reached.
Total function evaluations: 30
Total elapsed time: 3629.6678 seconds.
Total objective function evaluation time: 3432.0825
Best observed feasible point:
sigma lambda
______ __________
3.5161 4.6627e-07
Observed objective function value = 0.10016
Estimated objective function value = 0.10024
Function evaluation time = 69.1611
Best estimated feasible point (according to models):
sigma lambda
______ __________
3.6538 3.4072e-07
Estimated objective function value = 0.10024
Estimated function evaluation time = 68.4572
results =
BayesianOptimization with properties:
ObjectiveFcn: @(z)gather(loss(fitckernel(Ztrain,Ytrain,'KernelScale',z.sigma,'Lambda',z.lambda,'Verbose',0),Ztest,Ytest))
VariableDescriptions: [1×2 optimizableVariable]
Options: [1×1 struct]
MinObjective: 0.1002
XAtMinObjective: [1×2 table]
MinEstimatedObjective: 0.1002
XAtMinEstimatedObjective: [1×2 table]
NumObjectiveEvaluations: 30
TotalElapsedTime: 3.6297e+03
NextPoint: [1×2 table]
XTrace: [30×2 table]
ObjectiveTrace: [30×1 double]
ConstraintsTrace: []
UserDataTrace: {30×1 cell}
ObjectiveEvaluationTimeTrace: [30×1 double]
IterationTimeTrace: [30×1 double]
ErrorTrace: [30×1 double]
FeasibilityTrace: [30×1 logical]
FeasibilityProbabilityTrace: [30×1 double]
IndexOfMinimumTrace: [30×1 double]
ObjectiveMinimumTrace: [30×1 double]
EstimatedObjectiveMinimumTrace: [30×1 double]
Возвратите лучшую допустимую точку в модели Bayesian results при помощи функции bestPoint. Используйте критерий по умолчанию min-visited-upper-confidence-interval, который определяет лучшую допустимую точку как посещаемую точку, которая минимизирует верхний доверительный интервал на значении целевой функции.
zbest = bestPoint(results)
zbest=1×2 table
sigma lambda
______ __________
3.6538 3.4072e-07
Таблица zbest содержит оптимальные ориентировочные стоимости для аргументов пары "имя-значение" 'KernelScale' и 'Lambda'. Можно задать эти значения когда обучение новый оптимизированный классификатор ядра при помощи
Mdl = fitckernel(Ztrain,Ytrain,'KernelScale',zbest.sigma,'Lambda',zbest.lambda)
Для длинных массивов может занять много времени процедура оптимизации. Если набор данных является слишком большим, чтобы запустить процедуру оптимизации, можно попытаться оптимизировать параметры только при помощи частичных данных. Используйте datasample, функционируют и задают 'Replace','false' к выборочным данным без замены.
Смотрите также
bayesopt | bestPoint | cvpartition | datastore | fitckernel | gather | loss | nanmean | nanstd | optimizableVariable | tall