private IDataAnalysisProblem ExportProblem(IDataAnalysisProblem source)
{
var preprocessedProblem = (IDataAnalysisProblem)source.Clone();
preprocessedProblem.ProblemDataParameter.ActualValue = CreateNewProblemData();
return(preprocessedProblem);
}
private void openProblemButton_Click(object sender, EventArgs e)
{
openFileDialog.Title = "Open Problem";
if (openFileDialog.ShowDialog(this) == DialogResult.OK)
{
newProblemButton.Enabled = openProblemButton.Enabled = false;
algorithmProblemViewHost.Enabled = false;
ContentManager.LoadAsync(openFileDialog.FileName, delegate(IStorableContent content, Exception error) {
try {
if (error != null)
{
throw error;
}
IDataAnalysisProblem problem = content as IDataAnalysisProblem;
if (problem == null && (Content.Algorithm.ProblemType.IsAssignableFrom(content.GetType())))
{
Invoke(new Action(() =>
MessageBox.Show(this, "The selected file does not contain a DataAnalysisProblem problem.", "Invalid File", MessageBoxButtons.OK, MessageBoxIcon.Error)));
}
else
{
Content.Problem = problem;
}
} catch (Exception ex) {
Invoke(new Action(() => ErrorHandling.ShowErrorDialog(this, ex)));
} finally {
Invoke(new Action(delegate() {
algorithmProblemViewHost.Enabled = true;
newProblemButton.Enabled = openProblemButton.Enabled = true;
}));
}
});
}
}
private void SetNewProblemData(IDataAnalysisProblem problem)
{
var data = creator.CreateProblemData();
problem.ProblemDataParameter.ActualValue = data;
problem.Name = "Preprocessed " + problem.Name;
}
public PreprocessingContext(IDataAnalysisProblemData dataAnalysisProblemData, IAlgorithm algorithm, IDataAnalysisProblem problem) {
var transactionalPreprocessingData = new TransactionalPreprocessingData(dataAnalysisProblemData);
Data = new FilteredPreprocessingData(transactionalPreprocessingData);
ProblemData = dataAnalysisProblemData;
Algorithm = algorithm;
Problem = problem;
creator = new ProblemDataCreator(this);
}
private void algorithmProblemTabPage_DragDrop(object sender, DragEventArgs e)
{
if (e.Effect != DragDropEffects.None)
{
IDataAnalysisProblem problem = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat) as IDataAnalysisProblem;
if ((e.Effect & DragDropEffects.Copy) == DragDropEffects.Copy)
{
problem = (IDataAnalysisProblem)problem.Clone();
}
Content.Problem = problem;
}
}
public PreprocessingContext(IDataAnalysisProblemData dataAnalysisProblemData, IAlgorithm algorithm, IDataAnalysisProblem problem)
{
var transactionalPreprocessingData = new TransactionalPreprocessingData(dataAnalysisProblemData);
Data = new FilteredPreprocessingData(transactionalPreprocessingData);
ProblemData = dataAnalysisProblemData;
Algorithm = algorithm;
Problem = problem;
creator = new ProblemDataCreator(this);
}
private void GetMostOuterContent(Control control, out IAlgorithm algorithm, out IDataAnalysisProblem problem) {
algorithm = null;
problem = null;
while (control != null) {
var contentView = control as IContentView;
if (contentView != null) {
var newAlgorithm = contentView.Content as IAlgorithm;
if (newAlgorithm != null)
algorithm = newAlgorithm;
var newProblem = contentView.Content as IDataAnalysisProblem;
if (newProblem != null)
problem = newProblem;
}
control = control.Parent;
}
}
private void Algorithm_ProblemChanged(object sender, EventArgs e)
{
if (algorithm.Problem != null && !(algorithm.Problem is IDataAnalysisProblem))
{
algorithm.Problem = problem;
throw new ArgumentException("A cross validation algorithm can only contain DataAnalysisProblems.");
}
if (problem != null)
{
problem.Reset -= new EventHandler(Problem_Reset);
}
problem = (IDataAnalysisProblem)algorithm.Problem;
if (problem != null)
{
problem.Reset += new EventHandler(Problem_Reset);
}
OnProblemChanged();
}
private void GetMostOuterContent(Control control, out IAlgorithm algorithm, out IDataAnalysisProblem problem)
{
algorithm = null;
problem = null;
while (control != null)
{
var contentView = control as IContentView;
if (contentView != null)
{
var newAlgorithm = contentView.Content as IAlgorithm;
if (newAlgorithm != null)
{
algorithm = newAlgorithm;
}
var newProblem = contentView.Content as IDataAnalysisProblem;
if (newProblem != null)
{
problem = newProblem;
}
}
control = control.Parent;
}
}
protected GaussianProcessBase(IDataAnalysisProblem problem)
: base() {
Problem = problem;
Parameters.Add(new ValueParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function to use.", new MeanConst()));
Parameters.Add(new ValueParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceSquaredExponentialIso()));
Parameters.Add(new ValueParameter<IntValue>(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20)));
Parameters.Add(new ValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter<BoolValue>(ApproximateGradientsParameterName, "Indicates that gradients should not be approximated (necessary for LM-BFGS).", new BoolValue(false)));
Parameters[ApproximateGradientsParameterName].Hidden = true; // should not be changed
Parameters.Add(new FixedValueParameter<BoolValue>(ScaleInputValuesParameterName,
"Determines if the input variable values are scaled to the range [0..1] for training.", new BoolValue(true)));
Parameters[ScaleInputValuesParameterName].Hidden = true;
// necessary for BFGS
Parameters.Add(new ValueParameter<BoolValue>("Maximization", new BoolValue(false)));
Parameters["Maximization"].Hidden = true;
var randomCreator = new HeuristicLab.Random.RandomCreator();
var gpInitializer = new GaussianProcessHyperparameterInitializer();
var bfgsInitializer = new LbfgsInitializer();
var makeStep = new LbfgsMakeStep();
var branch = new ConditionalBranch();
var modelCreator = new Placeholder();
var updateResults = new LbfgsUpdateResults();
var analyzer = new LbfgsAnalyzer();
var finalModelCreator = new Placeholder();
var finalAnalyzer = new LbfgsAnalyzer();
var solutionCreator = new Placeholder();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.SeedParameter.ActualName = SeedParameterName;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = gpInitializer;
gpInitializer.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
gpInitializer.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
gpInitializer.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
gpInitializer.HyperparameterParameter.ActualName = HyperparameterParameterName;
gpInitializer.RandomParameter.ActualName = randomCreator.RandomParameter.Name;
gpInitializer.Successor = bfgsInitializer;
bfgsInitializer.IterationsParameter.ActualName = MinimizationIterationsParameterName;
bfgsInitializer.PointParameter.ActualName = HyperparameterParameterName;
bfgsInitializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
bfgsInitializer.Successor = makeStep;
makeStep.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
makeStep.PointParameter.ActualName = HyperparameterParameterName;
makeStep.Successor = branch;
branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
branch.FalseBranch = modelCreator;
branch.TrueBranch = finalModelCreator;
modelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
modelCreator.Successor = updateResults;
updateResults.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
updateResults.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
updateResults.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
updateResults.Successor = analyzer;
analyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
analyzer.PointParameter.ActualName = HyperparameterParameterName;
analyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
analyzer.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
analyzer.PointsTableParameter.ActualName = "Hyperparameter table";
analyzer.QualityGradientsTableParameter.ActualName = "Gradients table";
analyzer.QualitiesTableParameter.ActualName = "Negative log likelihood table";
analyzer.Successor = makeStep;
finalModelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
finalModelCreator.Successor = finalAnalyzer;
finalAnalyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
finalAnalyzer.PointParameter.ActualName = HyperparameterParameterName;
finalAnalyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
finalAnalyzer.PointsTableParameter.ActualName = analyzer.PointsTableParameter.ActualName;
finalAnalyzer.QualityGradientsTableParameter.ActualName = analyzer.QualityGradientsTableParameter.ActualName;
finalAnalyzer.QualitiesTableParameter.ActualName = analyzer.QualitiesTableParameter.ActualName;
finalAnalyzer.Successor = solutionCreator;
solutionCreator.OperatorParameter.ActualName = SolutionCreatorParameterName;
}
public void Start(CancellationToken cancellationToken)
{
lock (locker) {
if (startPending)
{
return;
}
startPending = true;
}
try {
if ((ExecutionState != ExecutionState.Prepared) && (ExecutionState != ExecutionState.Paused))
{
throw new InvalidOperationException(string.Format("Start not allowed in execution state \"{0}\".", ExecutionState));
}
seed = RandomSeedGenerator.GetSeed();
if (Algorithm == null)
{
return;
}
//create cloned algorithms
if (clonedAlgorithms.Count == 0)
{
int testSamplesCount = (SamplesEnd.Value - SamplesStart.Value) / Folds.Value;
IDataset shuffledDataset = null;
for (int i = 0; i < Folds.Value; i++)
{
var cloner = new Cloner();
if (ShuffleSamples.Value)
{
var random = new FastRandom(seed);
var dataAnalysisProblem = (IDataAnalysisProblem)algorithm.Problem;
var dataset = (Dataset)dataAnalysisProblem.ProblemData.Dataset;
shuffledDataset = shuffledDataset ?? dataset.Shuffle(random);
cloner.RegisterClonedObject(dataset, shuffledDataset);
}
IAlgorithm clonedAlgorithm = cloner.Clone(Algorithm);
clonedAlgorithm.Name = algorithm.Name + " Fold " + i;
IDataAnalysisProblem problem = clonedAlgorithm.Problem as IDataAnalysisProblem;
ISymbolicDataAnalysisProblem symbolicProblem = problem as ISymbolicDataAnalysisProblem;
int testStart = (i * testSamplesCount) + SamplesStart.Value;
int testEnd = (i + 1) == Folds.Value ? SamplesEnd.Value : (i + 1) * testSamplesCount + SamplesStart.Value;
problem.ProblemData.TrainingPartition.Start = SamplesStart.Value;
problem.ProblemData.TrainingPartition.End = SamplesEnd.Value;
problem.ProblemData.TestPartition.Start = testStart;
problem.ProblemData.TestPartition.End = testEnd;
DataAnalysisProblemData problemData = problem.ProblemData as DataAnalysisProblemData;
if (problemData != null)
{
problemData.TrainingPartitionParameter.Hidden = false;
problemData.TestPartitionParameter.Hidden = false;
}
if (symbolicProblem != null)
{
symbolicProblem.FitnessCalculationPartition.Start = SamplesStart.Value;
symbolicProblem.FitnessCalculationPartition.End = SamplesEnd.Value;
}
clonedAlgorithm.Prepare();
clonedAlgorithms.Add(clonedAlgorithm);
}
}
OnStarted();
} finally {
if (startPending)
{
startPending = false;
}
}
availableWorkers = new SemaphoreSlim(NumberOfWorkers.Value, NumberOfWorkers.Value);
allAlgorithmsFinished = new ManualResetEventSlim(false);
var startedTasks = new List <Task>(clonedAlgorithms.Count);
//start prepared or paused cloned algorithms
foreach (IAlgorithm clonedAlgorithm in clonedAlgorithms)
{
if (pausePending || stopPending || ExecutionState != ExecutionState.Started)
{
break;
}
if (clonedAlgorithm.ExecutionState == ExecutionState.Prepared ||
clonedAlgorithm.ExecutionState == ExecutionState.Paused)
{
availableWorkers.Wait();
lock (locker) {
if (pausePending || stopPending || ExecutionState != ExecutionState.Started)
{
break;
}
var task = clonedAlgorithm.StartAsync(cancellationToken);
startedTasks.Add(task);
}
}
}
allAlgorithmsFinished.Wait();
Task.WaitAll(startedTasks.ToArray()); // to get exceptions not handled within the tasks
}
public void Start()
{
if ((ExecutionState != ExecutionState.Prepared) && (ExecutionState != ExecutionState.Paused))
{
throw new InvalidOperationException(string.Format("Start not allowed in execution state \"{0}\".", ExecutionState));
}
if (Algorithm != null)
{
//create cloned algorithms
if (clonedAlgorithms.Count == 0)
{
int testSamplesCount = (SamplesEnd.Value - SamplesStart.Value) / Folds.Value;
for (int i = 0; i < Folds.Value; i++)
{
IAlgorithm clonedAlgorithm = (IAlgorithm)algorithm.Clone();
clonedAlgorithm.Name = algorithm.Name + " Fold " + i;
IDataAnalysisProblem problem = clonedAlgorithm.Problem as IDataAnalysisProblem;
ISymbolicDataAnalysisProblem symbolicProblem = problem as ISymbolicDataAnalysisProblem;
int testStart = (i * testSamplesCount) + SamplesStart.Value;
int testEnd = (i + 1) == Folds.Value ? SamplesEnd.Value : (i + 1) * testSamplesCount + SamplesStart.Value;
problem.ProblemData.TrainingPartition.Start = SamplesStart.Value;
problem.ProblemData.TrainingPartition.End = SamplesEnd.Value;
problem.ProblemData.TestPartition.Start = testStart;
problem.ProblemData.TestPartition.End = testEnd;
DataAnalysisProblemData problemData = problem.ProblemData as DataAnalysisProblemData;
if (problemData != null)
{
problemData.TrainingPartitionParameter.Hidden = false;
problemData.TestPartitionParameter.Hidden = false;
}
if (symbolicProblem != null)
{
symbolicProblem.FitnessCalculationPartition.Start = SamplesStart.Value;
symbolicProblem.FitnessCalculationPartition.End = SamplesEnd.Value;
}
clonedAlgorithm.Prepare();
clonedAlgorithms.Add(clonedAlgorithm);
}
}
//start prepared or paused cloned algorithms
int startedAlgorithms = 0;
foreach (IAlgorithm clonedAlgorithm in clonedAlgorithms)
{
if (startedAlgorithms < NumberOfWorkers.Value)
{
if (clonedAlgorithm.ExecutionState == ExecutionState.Prepared ||
clonedAlgorithm.ExecutionState == ExecutionState.Paused)
{
// start and wait until the alg is started
using (var signal = new ManualResetEvent(false)) {
EventHandler signalSetter = (sender, args) => { signal.Set(); };
clonedAlgorithm.Started += signalSetter;
clonedAlgorithm.Start();
signal.WaitOne();
clonedAlgorithm.Started -= signalSetter;
startedAlgorithms++;
}
}
}
}
OnStarted();
}
}
private IDataAnalysisProblem ExportProblem(IDataAnalysisProblem source) {
var preprocessedProblem = (IDataAnalysisProblem)source.Clone();
preprocessedProblem.ProblemDataParameter.ActualValue = CreateNewProblemData();
return preprocessedProblem;
}
protected GaussianProcessBase(IDataAnalysisProblem problem)
: base()
{
Problem = problem;
Parameters.Add(new ValueParameter <IMeanFunction>(MeanFunctionParameterName, "The mean function to use.", new MeanConst()));
Parameters.Add(new ValueParameter <ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceSquaredExponentialIso()));
Parameters.Add(new ValueParameter <IntValue>(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20)));
Parameters.Add(new ValueParameter <IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter <BoolValue>(ApproximateGradientsParameterName, "Indicates that gradients should not be approximated (necessary for LM-BFGS).", new BoolValue(false)));
Parameters[ApproximateGradientsParameterName].Hidden = true; // should not be changed
Parameters.Add(new FixedValueParameter <BoolValue>(ScaleInputValuesParameterName,
"Determines if the input variable values are scaled to the range [0..1] for training.", new BoolValue(true)));
Parameters[ScaleInputValuesParameterName].Hidden = true;
// necessary for BFGS
Parameters.Add(new FixedValueParameter <BoolValue>("Maximization (BFGS)", new BoolValue(false)));
Parameters["Maximization (BFGS)"].Hidden = true;
var randomCreator = new HeuristicLab.Random.RandomCreator();
var gpInitializer = new GaussianProcessHyperparameterInitializer();
var bfgsInitializer = new LbfgsInitializer();
var makeStep = new LbfgsMakeStep();
var branch = new ConditionalBranch();
var modelCreator = new Placeholder();
var updateResults = new LbfgsUpdateResults();
var analyzer = new LbfgsAnalyzer();
var finalModelCreator = new Placeholder();
var finalAnalyzer = new LbfgsAnalyzer();
var solutionCreator = new Placeholder();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.SeedParameter.ActualName = SeedParameterName;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = gpInitializer;
gpInitializer.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
gpInitializer.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
gpInitializer.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
gpInitializer.HyperparameterParameter.ActualName = HyperparameterParameterName;
gpInitializer.RandomParameter.ActualName = randomCreator.RandomParameter.Name;
gpInitializer.Successor = bfgsInitializer;
bfgsInitializer.IterationsParameter.ActualName = MinimizationIterationsParameterName;
bfgsInitializer.PointParameter.ActualName = HyperparameterParameterName;
bfgsInitializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
bfgsInitializer.Successor = makeStep;
makeStep.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
makeStep.PointParameter.ActualName = HyperparameterParameterName;
makeStep.Successor = branch;
branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
branch.FalseBranch = modelCreator;
branch.TrueBranch = finalModelCreator;
modelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
modelCreator.Successor = updateResults;
updateResults.MaximizationParameter.ActualName = "Maximization (BFGS)";
updateResults.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
updateResults.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
updateResults.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
updateResults.Successor = analyzer;
analyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
analyzer.PointParameter.ActualName = HyperparameterParameterName;
analyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
analyzer.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
analyzer.PointsTableParameter.ActualName = "Hyperparameter table";
analyzer.QualityGradientsTableParameter.ActualName = "Gradients table";
analyzer.QualitiesTableParameter.ActualName = "Negative log likelihood table";
analyzer.Successor = makeStep;
finalModelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
finalModelCreator.Successor = finalAnalyzer;
finalAnalyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
finalAnalyzer.PointParameter.ActualName = HyperparameterParameterName;
finalAnalyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
finalAnalyzer.PointsTableParameter.ActualName = analyzer.PointsTableParameter.ActualName;
finalAnalyzer.QualityGradientsTableParameter.ActualName = analyzer.QualityGradientsTableParameter.ActualName;
finalAnalyzer.QualitiesTableParameter.ActualName = analyzer.QualitiesTableParameter.ActualName;
finalAnalyzer.Successor = solutionCreator;
solutionCreator.OperatorParameter.ActualName = SolutionCreatorParameterName;
}
private void SetNewProblemData(IDataAnalysisProblem problem) {
var data = creator.CreateProblemData();
problem.ProblemDataParameter.ActualValue = data;
problem.Name = "Preprocessed " + problem.Name;
}
