/// <summary>
/// Bayesian optimization (BO) for global black box optimization problems. BO learns a model based on the initial parameter sets and scores.
/// This model is used to sample new promising parameter candiates which are evaluated and added to the existing paramter sets.
/// This process iterates several times. The method is computational expensive so is most relevant for expensive problems,
/// where each evaluation of the function to minimize takes a long time, like hyper parameter tuning a machine learning method.
/// But in that case it can usually reduce the number of iterations required to reach a good solution compared to less sophisticated methods.
/// Implementation loosely based on:
/// http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf
/// https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
/// https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
/// </summary>
/// <param name="parameters">A list of parameter bounds for each optimization parameter</param>
/// <param name="maxIterations">Maximum number of iterations. MaxIteration * numberOfCandidatesEvaluatedPrIteration = totalFunctionEvaluations</param>
/// <param name="numberOfStartingPoints">Number of randomly created starting points to use for the initial model in the first iteration (default is 5)</param>
/// <param name="numberOfCandidatesEvaluatedPrIteration">How many candiate parameter set should by sampled from the model in each iteration.
/// The parameter sets are inlcuded in order of most promissing outcome (default is 1)</param>
/// <param name="seed">Seed for the random initialization</param>
public BayesianOptimizer(ParameterBounds[] parameters, int maxIterations, int numberOfStartingPoints = 5, int numberOfCandidatesEvaluatedPrIteration = 1, int seed = 42)
{
if (parameters == null)
{
throw new ArgumentNullException("parameters");
}
if (maxIterations <= 0)
{
throw new ArgumentNullException("maxIterations must be at least 1");
}
if (numberOfStartingPoints < 1)
{
throw new ArgumentNullException("numberOfParticles must be at least 1");
}
m_parameters = parameters;
m_maxIterations = maxIterations;
m_numberOfStartingPoints = numberOfStartingPoints;
m_numberOfCandidatesEvaluatedPrIteration = numberOfCandidatesEvaluatedPrIteration;
m_sampler = new RandomUniform(seed);
// Hyper parameters for regression extra trees learner. These are based on the values suggested in http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf.
// However, according to the author Frank Hutter, the hyper parameters for the forest model should not matter that much.
m_learner = new RegressionExtremelyRandomizedTreesLearner(30, 10, 2000, parameters.Length, 1e-6, 1.0, 42, false);
// optimizer for finding maximum expectation (most promissing hyper parameters) from extra trees model.
m_maximizer = new RandomSearchOptimizer(m_parameters, 1000, 42, false);
// acquisition function to maximize,
m_acquisitionFunc = AcquisitionFunctions.ExpectedImprovement;
}
/// <summary>
/// Implementation of the SMAC algorithm for hyperparameter optimization.
/// Based on: Sequential Model-Based Optimization for General Algorithm Configuration:
/// https://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf
/// Uses Bayesian optimization in tandem with a greedy local search on the top performing solutions.
/// And ML.Net implementation:
/// https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Sweeper/Algorithms/SmacSweeper.cs
/// </summary>
/// <param name="parameters">A list of parameter specs, one for each optimization parameter</param>
/// <param name="iterations">The number of iterations to perform.
/// Iteration * functionEvaluationsPerIteration = totalFunctionEvaluations</param>
/// <param name="randomStartingPointCount">Number of randomly parameter sets used
/// for initialization (default is 20)</param>
/// <param name="functionEvaluationsPerIterationCount">The number of function evaluations per iteration.
/// The parameter sets are included in order of most promising outcome (default is 1)</param>
/// <param name="localSearchPointCount">The number of top contenders
/// to use in the greedy local search (default is (10)</param>
/// <param name="randomSearchPointCount">The number of random parameter sets
/// used when maximizing the expected improvement acquisition function (default is 1000)</param>
/// <param name="epsilon">Threshold for ending local search (default is 0.00001)</param>
/// <param name="seed"></param>
public SmacOptimizer(IParameterSpec[] parameters,
int iterations,
int randomStartingPointCount = 20,
int functionEvaluationsPerIterationCount = 1,
int localSearchPointCount = 10,
int randomSearchPointCount = 1000,
double epsilon = 0.00001,
int seed = 42)
{
m_parameters = parameters ?? throw new ArgumentNullException(nameof(parameters));
if (iterations < 1)
{
throw new ArgumentException(nameof(iterations) +
"must be at least 1. Was: " + iterations);
}
if (randomStartingPointCount < 1)
{
throw new ArgumentException(nameof(randomStartingPointCount) +
"must be at least 1. Was: " + randomStartingPointCount);
}
if (functionEvaluationsPerIterationCount < 1)
{
throw new ArgumentException(nameof(functionEvaluationsPerIterationCount) +
"must be at least 1. Was: " + functionEvaluationsPerIterationCount);
}
if (localSearchPointCount < 1)
{
throw new ArgumentException(nameof(localSearchPointCount) +
"must be at least 1. Was: " + localSearchPointCount);
}
if (randomSearchPointCount < 1)
{
throw new ArgumentException(nameof(randomSearchPointCount) +
"must be at least 1. Was: " + randomSearchPointCount);
}
m_random = new Random(seed);
// Use member to seed the random uniform sampler.
m_sampler = new RandomUniform(m_random.Next());
m_iterations = iterations;
m_randomStartingPointsCount = randomStartingPointCount;
m_functionEvaluationsPerIterationCount = functionEvaluationsPerIterationCount;
m_localSearchPointCount = localSearchPointCount;
m_randomSearchPointCount = randomSearchPointCount;
m_epsilon = epsilon;
// Hyper parameters for regression extra trees learner.
// These are based on the values suggested in http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf.
// However, according to the author Frank Hutter,
// the hyper parameters for the forest model should not matter that much.
m_learner = new RegressionExtremelyRandomizedTreesLearner(trees: 10,
minimumSplitSize: 2,
maximumTreeDepth: 2000,
featuresPrSplit: parameters.Length,
minimumInformationGain: 1e-6,
subSampleRatio: 1.0,
seed: m_random.Next(), // Use member to seed the random uniform sampler.
runParallel: false);
}
/// <summary>
/// Bayesian optimization (BO) for global black box optimization problems. BO learns a model based on the initial parameter sets and scores.
/// This model is used to sample new promising parameter candiates which are evaluated and added to the existing paramter sets.
/// This process iterates several times. The method is computational expensive so is most relevant for expensive problems,
/// where each evaluation of the function to minimize takes a long time, like hyper parameter tuning a machine learning method.
/// But in that case it can usually reduce the number of iterations required to reach a good solution compared to less sophisticated methods.
/// Implementation loosely based on:
/// http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf
/// https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
/// https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
/// </summary>
/// <param name="parameters">A list of parameter bounds for each optimization parameter</param>
/// <param name="maxIterations">Maximum number of iterations. MaxIteration * numberOfCandidatesEvaluatedPrIteration = totalFunctionEvaluations</param>
/// <param name="previousParameterSets">Parameter sets from previous run</param>
/// <param name="previousParameterSetScores">Scores from from previous run corresponding to each parameter set</param>
/// <param name="numberOfCandidatesEvaluatedPrIteration">How many candiate parameter set should by sampled from the model in each iteration.
/// The parameter sets are inlcuded in order of most promissing outcome (default is 1)</param>
/// <param name="seed">Seed for the random initialization</param>
public BayesianOptimizer(ParameterBounds[] parameters, int maxIterations, List <double[]> previousParameterSets, List <double> previousParameterSetScores,
int numberOfCandidatesEvaluatedPrIteration = 1, int seed = 42)
{
if (parameters == null)
{
throw new ArgumentNullException("parameters");
}
if (maxIterations <= 0)
{
throw new ArgumentNullException("maxIterations must be at least 1");
}
if (previousParameterSets == null)
{
throw new ArgumentNullException("previousParameterSets");
}
if (previousParameterSetScores == null)
{
throw new ArgumentNullException("previousResults");
}
if (previousParameterSets.Count != previousParameterSetScores.Count)
{
throw new ArgumentException("previousParameterSets length: "
+ previousParameterSets.Count + " does not correspond with previousResults length: " + previousParameterSetScores.Count);
}
if (previousParameterSetScores.Count < 2 || previousParameterSets.Count < 2)
{
throw new ArgumentException("previousParameterSets length and previousResults length must be at least 2 and was: " + previousParameterSetScores.Count);
}
m_parameters = parameters;
m_maxIterations = maxIterations;
m_numberOfCandidatesEvaluatedPrIteration = numberOfCandidatesEvaluatedPrIteration;
m_random = new Random(seed);
// Use member to seed the random uniform sampler.
m_sampler = new RandomUniform(m_random.Next());
// Hyper parameters for regression extra trees learner. These are based on the values suggested in http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf.
// However, according to the author Frank Hutter, the hyper parameters for the forest model should not matter that much.
m_learner = new RegressionExtremelyRandomizedTreesLearner(trees: 30,
minimumSplitSize: 10,
maximumTreeDepth: 2000,
featuresPrSplit: parameters.Length,
minimumInformationGain: 1e-6,
subSampleRatio: 1.0,
seed: m_random.Next(), // Use member to seed the random uniform sampler.
runParallel: false);
// Optimizer for finding maximum expectation (most promissing hyper parameters) from extra trees model.
m_maximizer = new RandomSearchOptimizer(m_parameters, iterations: 1000,
seed: m_random.Next(), // Use member to seed the random uniform sampler.
runParallel: false);
// Acquisition function to maximize.
m_acquisitionFunc = AcquisitionFunctions.ExpectedImprovement;
m_previousParameterSets = previousParameterSets;
m_previousParameterSetScores = previousParameterSetScores;
}
/// <summary>
/// Bayesian optimization (BO) for global black box optimization problems. BO learns a model based on the initial parameter sets and scores.
/// This model is used to sample new promising parameter candidates which are evaluated and added to the existing parameter sets.
/// This process iterates several times. The method is computational expensive so is most relevant for expensive problems,
/// where each evaluation of the function to minimize takes a long time, like hyper parameter tuning a machine learning method.
/// But in that case it can usually reduce the number of iterations required to reach a good solution compared to less sophisticated methods.
/// Implementation loosely based on:
/// http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf
/// https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
/// https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
/// </summary>
/// <param name="parameters">A list of parameter specs, one for each optimization parameter</param>
/// <param name="iterations">Number of iterations. Iteration * functionEvaluationsPerIteration = totalFunctionEvaluations</param>
/// <param name="randomStartingPointCount">Number of randomly created starting points to use for the initial model in the first iteration (default is 5)</param>
/// <param name="functionEvaluationsPerIteration">The number of function evaluations per iteration.
/// The parameter sets are included in order of most promising outcome (default is 1)</param>
/// <param name="seed">Seed for the random initialization</param>
/// <param name="maxDegreeOfParallelism">Maximum number of concurrent operations. Default is -1 (unlimited)</param>
/// <param name="allowMultipleEvaluations">Enables re-evaluation of duplicate parameter sets for non-deterministic functions</param>
public BayesianOptimizer(IParameterSpec[] parameters,
int iterations,
int randomStartingPointCount = 5,
int functionEvaluationsPerIteration = 1,
int seed = 42,
int maxDegreeOfParallelism = -1,
bool allowMultipleEvaluations = false)
{
if (iterations <= 0)
{
throw new ArgumentException("maxIterations must be at least 1");
}
if (randomStartingPointCount < 1)
{
throw new ArgumentException("numberOfParticles must be at least 1");
}
m_parameters = parameters ?? throw new ArgumentNullException(nameof(parameters));
m_iterations = iterations;
m_randomStartingPointCount = randomStartingPointCount;
m_functionEvaluationsPerIteration = functionEvaluationsPerIteration;
m_runParallel = maxDegreeOfParallelism != 1;
m_parallelOptions = new ParallelOptions {
MaxDegreeOfParallelism = maxDegreeOfParallelism
};
m_allowMultipleEvaluations = allowMultipleEvaluations;
m_locker = new object();
m_random = new Random(seed);
// Use member to seed the random uniform sampler.
m_sampler = new RandomUniform(m_random.Next());
// Hyper parameters for regression extra trees learner. These are based on the values suggested in http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf.
// However, according to the author Frank Hutter, the hyper parameters for the forest model should not matter that much.
m_learner = new RegressionExtremelyRandomizedTreesLearner(trees: 30,
minimumSplitSize: 10,
maximumTreeDepth: 2000,
featuresPrSplit: parameters.Length,
minimumInformationGain: 1e-6,
subSampleRatio: 1.0,
seed: m_random.Next(), // Use member to seed the random uniform sampler.
runParallel: m_runParallel);
// Optimizer for finding maximum expectation (most promising hyper parameters) from extra trees model.
m_maximizer = new RandomSearchOptimizer(m_parameters, iterations: 1000,
seed: m_random.Next(), // Use member to seed the random uniform sampler.
runParallel: maxDegreeOfParallelism > 1);
// Acquisition function to maximize.
m_acquisitionFunc = AcquisitionFunctions.ExpectedImprovement;
}
double RegressionExtremelyRandomizedTreesLearner_Learn_Aptitude(int trees, double subSampleRatio = 1.0)
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new RegressionExtremelyRandomizedTreesLearner(trees, 1, 100, 1, 0.0001, subSampleRatio, 42, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
return(error);
}
/// <summary>
/// Bayesian optimization (BO) for global black box optimization problems. BO learns a model based on the initial parameter sets and scores.
/// This model is used to sample new promising parameter candiates which are evaluated and added to the existing paramter sets.
/// This process iterates several times. The method is computational expensive so is most relevant for expensive problems,
/// where each evaluation of the function to minimize takes a long time, like hyper parameter tuning a machine learning method.
/// But in that case it can usually reduce the number of iterations required to reach a good solution compared to less sophisticated methods.
/// Implementation loosely based on:
/// http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf
/// https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
/// https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
/// </summary>
/// <param name="parameters">A list of parameter bounds for each optimization parameter</param>
/// <param name="maxIterations">Maximum number of iterations. MaxIteration * numberOfCandidatesEvaluatedPrIteration = totalFunctionEvaluations</param>
/// <param name="previousParameterSets">Parameter sets from previous run</param>
/// <param name="previousParameterSetScores">Scores from from previous run corresponding to each parameter set</param>
/// <param name="numberOfCandidatesEvaluatedPrIteration">How many candiate parameter set should by sampled from the model in each iteration.
/// The parameter sets are inlcuded in order of most promissing outcome (default is 1)</param>
/// <param name="seed">Seed for the random initialization</param>
public BayesianOptimizer(ParameterBounds[] parameters, int maxIterations, List <double[]> previousParameterSets, List <double> previousParameterSetScores,
int numberOfCandidatesEvaluatedPrIteration = 1, int seed = 42)
{
if (parameters == null)
{
throw new ArgumentNullException("parameters");
}
if (maxIterations <= 0)
{
throw new ArgumentNullException("maxIterations must be at least 1");
}
if (previousParameterSets == null)
{
throw new ArgumentNullException("previousParameterSets");
}
if (previousParameterSetScores == null)
{
throw new ArgumentNullException("previousResults");
}
if (previousParameterSets.Count != previousParameterSetScores.Count)
{
throw new ArgumentException("previousParameterSets length: "
+ previousParameterSets.Count + " does not correspond with previousResults length: " + previousParameterSetScores.Count);
}
if (previousParameterSetScores.Count < 2 || previousParameterSets.Count < 2)
{
throw new ArgumentException("previousParameterSets length and previousResults length must be at least 2 and was: " + previousParameterSetScores.Count);
}
m_parameters = parameters;
m_maxIterations = maxIterations;
m_numberOfCandidatesEvaluatedPrIteration = numberOfCandidatesEvaluatedPrIteration;
m_sampler = new RandomUniform(seed);
// Hyper parameters for regression extra trees learner. These are based on the values suggested in http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf.
// However, according to the author Frank Hutter, the hyper parameters for the forest model should not matter that much.
m_learner = new RegressionExtremelyRandomizedTreesLearner(30, 10, 2000, parameters.Length, 1e-6, 1.0, 42, false);
// optimizer for finding maximum expectation (most promissing hyper parameters) from random forest model
m_maximizer = new RandomSearchOptimizer(m_parameters, 1000, 42, false);
// acquisition function to maximize,
m_acquisitionFunc = AcquisitionFunctions.ExpectedImprovement;
m_previousParameterSets = previousParameterSets;
m_previousParameterSetScores = previousParameterSetScores;
}
public void RegressionExtremelyRandomizedTreesLearnerTest_Learn_Glass_100_Trees_Parallel()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, true);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.33450356466453129, error, m_delta);
}
double RegressionExtremelyRandomizedTreesLearner_Learn_Aptitude(int trees, double subSampleRatio = 1.0)
{
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var rows = targets.Length;
var sut = new RegressionExtremelyRandomizedTreesLearner(trees, 1, 100, 1, 0.0001, subSampleRatio, 42, false);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
return(error);
}
public void RegressionExtremelyRandomizedTreesLearnerTest_Learn_Glass_100_Trees_Parallel()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var sut = new RegressionExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, true);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.33450356466453129, error, m_delta);
}
public void RegressionExtremelyRandomizedTreesLearnerTest_Learn_Glass_100_Indices()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new RegressionExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, false);
var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.592240619161302, error, m_delta);
}
public void RegressionExtremelyRandomizedTreesLearnerTest_Learn_Glass_100_Indices()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var rows = targets.Length;
var sut = new RegressionExtremelyRandomizedTreesLearner(100, 1, 100, 1, 0.0001, 1.0, 42, false);
var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.622380056587391, error, 0.0000001);
}
/// <summary>
/// Bayesian optimization (BO) for global black box optimization problems. BO learns a model based on the initial
/// parameter sets and scores.
/// This model is used to sample new promising parameter candidates which are evaluated and added to the existing
/// parameter sets.
/// This process iterates several times. The method is computational expensive so is most relevant for expensive
/// problems,
/// where each evaluation of the function to minimize takes a long time, like hyper parameter tuning a machine learning
/// method.
/// But in that case it can usually reduce the number of iterations required to reach a good solution compared to less
/// sophisticated methods.
/// Implementation loosely based on:
/// http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf
/// https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
/// https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
/// </summary>
/// <param name="parameters">A list of parameter specs, one for each optimization parameter</param>
/// <param name="iterations">
/// The number of iterations to perform.
/// Iteration * functionEvaluationsPerIteration = totalFunctionEvaluations
/// </param>
/// <param name="randomStartingPointCount">
/// Number of randomly parameter sets used
/// for initialization (default is 20)
/// </param>
/// <param name="functionEvaluationsPerIterationCount">
/// The number of function evaluations per iteration.
/// The parameter sets are included in order of most promising outcome (default is 1)
/// </param>
/// <param name="randomSearchPointCount">
/// The number of random parameter sets
/// used when maximizing the expected improvement acquisition function (default is 1000)
/// </param>
/// <param name="seed"></param>
/// <param name="runParallel">
/// Use multi threading to speed up execution (default is false).
/// Note that the order of results returned the Optimize method will not be reproducible when running in parallel.
/// Results will be the same, only the order is not reproducible
/// </param>
/// <param name="maxDegreeOfParallelism">Maximum number of concurrent operations (default is -1 (unlimited))</param>
public BayesianOptimizer(
IParameterSpec[] parameters,
int iterations,
int randomStartingPointCount = 5,
int functionEvaluationsPerIterationCount = 1,
int randomSearchPointCount = 1000,
int seed = 42,
bool runParallel = false,
int maxDegreeOfParallelism = -1)
{
m_parameters = parameters ?? throw new ArgumentNullException(nameof(parameters));
if (iterations < 1)
{
throw new ArgumentException(nameof(iterations) + "must be at least 1. Was: " + iterations);
}
if (randomStartingPointCount < 1)
{
throw new ArgumentException(
nameof(randomStartingPointCount) + "must be at least 1. Was: " + randomStartingPointCount
);
}
if (functionEvaluationsPerIterationCount < 1)
{
throw new ArgumentException(
nameof(functionEvaluationsPerIterationCount) +
"must be at least 1. Was: " +
functionEvaluationsPerIterationCount
);
}
if (randomSearchPointCount < 1)
{
throw new ArgumentException(
nameof(randomSearchPointCount) + "must be at least 1. Was: " + randomSearchPointCount
);
}
m_random = new Random(seed);
// Use member to seed the random uniform sampler.
m_sampler = new RandomUniform(m_random.Next());
m_iterations = iterations;
m_randomStartingPointsCount = randomStartingPointCount;
m_functionEvaluationsPerIterationCount = functionEvaluationsPerIterationCount;
m_randomSearchPointCount = randomSearchPointCount;
// Hyper parameters for regression extra trees learner.
// These are based on the values suggested in http://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf.
// However, according to the author Frank Hutter,
// the hyper parameters for the forest model should not matter that much.
m_learner = new RegressionExtremelyRandomizedTreesLearner(
30,
10,
2000,
parameters.Length,
1e-6,
1.0,
m_random.Next(), // Use member to seed the random uniform sampler.
false
);
m_runParallel = runParallel;
m_maxDegreeOfParallelism = maxDegreeOfParallelism;
}
