Changeset 7939 for branches/DataAnalysisService/HeuristicLab.ExperimentGeneration.DataAnalysis.ExperimentWizard/3.3/MediumAnalysisPage.cs

Timestamp:

05/30/12 17:24:16 (12 years ago)

Author:

spimming

Message:

#1807:

• wizard page to view generated experiments
• generate experiments according to example
• plugin frame adapted
• new version of problem view dialog included

File:

• branches/DataAnalysisService/HeuristicLab.ExperimentGeneration.DataAnalysis.ExperimentWizard/3.3/MediumAnalysisPage.cs (modified) (3 diffs)

branches/DataAnalysisService/HeuristicLab.ExperimentGeneration.DataAnalysis.ExperimentWizard/3.3/MediumAnalysisPage.cs

@@ -3 +3 @@
 using System.Linq;
 using HeuristicLab.Algorithms.DataAnalysis;
+using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm;
+using HeuristicLab.Common;
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.ParameterConfigurationTreeEncoding;
 using HeuristicLab.Optimization;
 using HeuristicLab.Problems.DataAnalysis;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
 namespace HeuristicLab.ExperimentGeneration.DataAnalysis.ExperimentWizard {
   public partial class MediumAnalysisPage : HeuristicLab.ExperimentGeneration.DataAnalysis.Wizard.WizardPage {
+    private const int nrOfRepetitions = 10;
+    private const string gaCrossover = "SubtreeSwappingCrossover";
+    private const string gaMutator = "MultiSymbolicExpressionTreeManipulator";
+    private const string gaSelector = "GenderSpecificSelection";
+
     private BackgroundWorker worker;
 
@@ -22 +30 @@
       worker.DoWork += new DoWorkEventHandler(GenerateExperimentsTask);
       worker.RunWorkerCompleted += new RunWorkerCompletedEventHandler(WorkerCompleted);
+      worker.WorkerReportsProgress = true;
+      worker.ProgressChanged += new ProgressChangedEventHandler(WorkerProgressChanged);
 
     }
@@ -28 +38 @@
       DataAnalysisWizardContext c = e.Argument as DataAnalysisWizardContext;
       IProblem problem = c.Problem;
+
+      // SVR Experiment -------------------------------------------------------
       SupportVectorRegression svr = new SupportVectorRegression();
       svr.Problem = (IRegressionProblem)problem;
       ParameterConfigurationTree vc = new ParameterConfigurationTree(svr, problem);
-      var nuPc = vc.AlgorithmConfiguration.ParameterConfigurations.Where(x => x.ParameterName == "Nu").SingleOrDefault();
-      nuPc.Optimize = true;
-      var nuVc = (RangeValueConfiguration)nuPc.ValueConfigurations.First();
-      nuVc.Optimize = true;
-      nuVc.RangeConstraint = new DoubleValueRange(new DoubleValue(0.1), new DoubleValue(0.9), new DoubleValue(0.1));
 
-      var costPc = vc.AlgorithmConfiguration.ParameterConfigurations.Where(x => x.ParameterName == "Cost").SingleOrDefault();
-      costPc.Optimize = true;
-      var costVc = (RangeValueConfiguration)costPc.ValueConfigurations.First();
-      costVc.Optimize = true;
-      costVc.RangeConstraint = new DoubleValueFactorRange(new DoubleValue(0.03125), new DoubleValue(32768), new DoubleValue(4));
+      var nuRange = new DoubleValueRange(new DoubleValue(0.1), new DoubleValue(0.9), new DoubleValue(0.1));
+      SetParameterRangeContraint(vc, "Nu", nuRange);
 
-      var gammaPc = vc.AlgorithmConfiguration.ParameterConfigurations.Where(x => x.ParameterName == "Gamma").SingleOrDefault();
-      gammaPc.Optimize = true;
-      var gammaVc = (RangeValueConfiguration)gammaPc.ValueConfigurations.First();
-      gammaVc.Optimize = true;
-      gammaVc.RangeConstraint = new DoubleValueFactorRange(new DoubleValue(6.10352E-05), new DoubleValue(64), new DoubleValue(4));
+      var costRange = new DoubleValueFactorRange(new DoubleValue(0.03125), new DoubleValue(32768), new DoubleValue(4));
+      SetParameterRangeContraint(vc, "Cost", costRange);
 
-      Experiment experiment = vc.GenerateExperiment(svr);
+      var gammaRange = new DoubleValueFactorRange(new DoubleValue(6.10352E-05), new DoubleValue(64), new DoubleValue(4));
+      SetParameterRangeContraint(vc, "Gamma", gammaRange);
+      worker.ReportProgress(5);
+      Experiment svrExperiment = vc.GenerateExperiment(svr);
+      // ======================================================================
+      worker.ReportProgress(20);
+
+      // RF Experiment --------------------------------------------------------
+      RandomForestRegression rfr = new RandomForestRegression();
+      rfr.Problem = (IRegressionProblem)problem;
+      ParameterConfigurationTree rfConfig = new ParameterConfigurationTree(rfr, problem);
+      var rRange = new DoubleValueRange(new DoubleValue(0.1), new DoubleValue(0.7), new DoubleValue(0.1));
+      SetParameterRangeContraint(rfConfig, "R", rRange);
+      var treeRange = new IntValueRange(new IntValue(250), new IntValue(250), new IntValue(250));
+      SetParameterRangeContraint(rfConfig, "Number of trees", treeRange);
+      Experiment rfrExperiment = rfConfig.GenerateExperiment(rfr);
+      // ======================================================================
+      worker.ReportProgress(40);
+      // NN Experiment --------------------------------------------------------
+      NeuralNetworkRegression nnr = new NeuralNetworkRegression();
+      nnr.Problem = (IRegressionProblem)problem;
+      ParameterConfigurationTree nnrConfig = new ParameterConfigurationTree(nnr, problem);
+      var decayRange = new DoubleValueFactorRange(new DoubleValue(0.001), new DoubleValue(100), new DoubleValue(10));
+      SetParameterRangeContraint(nnrConfig, "Decay", decayRange);
+      var nodesRange = new IntValueFactorRange(new IntValue(1), new IntValue(100), new IntValue(3));
+      SetParameterRangeContraint(nnrConfig, "NodesInFirstHiddenLayer", nodesRange);
+      Experiment nnrExperiment = nnrConfig.GenerateExperiment(nnr);
+      // ======================================================================
+      worker.ReportProgress(60);
+      // GP Experiment --------------------------------------------------------
+      OffspringSelectionGeneticAlgorithm osga = new OffspringSelectionGeneticAlgorithm();
+      var prob = new SymbolicRegressionSingleObjectiveProblem();
+      prob.ProblemData = ((IRegressionProblem)problem).ProblemData;
+      //prob.SolutionCreator = new MultiSymbolicDataAnalysisExpressionCreator();
+      osga.Problem = prob;
+      osga.ComparisonFactorLowerBound.Value = 1;
+      osga.Crossover = osga.Problem.Operators.OfType<ICrossover>().FirstOrDefault(x => x.Name == gaCrossover);
+      osga.MaximumEvaluatedSolutions.Value = 500000;
+      osga.MaximumGenerations.Value = 100;
+      osga.MutationProbability = new PercentValue(0.15);
+      osga.Mutator = osga.Problem.Operators.OfType<IManipulator>().FirstOrDefault(x => x.Name == gaMutator);
+      osga.PopulationSize.Value = 500;
+      osga.Selector = osga.SelectorParameter.ValidValues.FirstOrDefault(x => x.Name == gaSelector);
+
+      CrossValidation crossSmall = new CrossValidation();
+      crossSmall.Algorithm = osga;
+      BatchRun batchSmall = new BatchRun("small");
+      batchSmall.Repetitions = nrOfRepetitions;
+      batchSmall.Optimizer = crossSmall;
+
+      CrossValidation crossMedium = (CrossValidation)crossSmall.Clone(new Cloner());
+      var probMedium = (SymbolicRegressionSingleObjectiveProblem)prob.Clone(new Cloner());
+      probMedium.MaximumSymbolicExpressionTreeDepth.Value = 12;
+      probMedium.MaximumSymbolicExpressionTreeLength.Value = 125;
+      crossMedium.Algorithm.Problem = probMedium;
+      BatchRun batchMedium = new BatchRun("medium");
+      batchMedium.Repetitions = nrOfRepetitions;
+      batchMedium.Optimizer = crossMedium;
+
+      CrossValidation crossLarge = (CrossValidation)crossSmall.Clone(new Cloner());
+      var probLarge = (SymbolicRegressionSingleObjectiveProblem)prob.Clone(new Cloner());
+      probLarge.MaximumSymbolicExpressionTreeDepth.Value = 17;
+      probLarge.MaximumSymbolicExpressionTreeLength.Value = 250;
+      crossLarge.Algorithm.Problem = probLarge;
+      BatchRun batchLarge = new BatchRun("large");
+      batchLarge.Repetitions = nrOfRepetitions;
+      batchLarge.Optimizer = crossLarge;
+
+      Experiment gpExperiment = new Experiment();
+      gpExperiment.Optimizers.Add(batchSmall);
+      gpExperiment.Optimizers.Add(batchMedium);
+      gpExperiment.Optimizers.Add(batchLarge);
+      // ======================================================================
+      worker.ReportProgress(80);
+
+      Experiment experiment = new Experiment("Data Analysis Experiment");
+      experiment.Optimizers.Add(svrExperiment);
+      experiment.Optimizers.Add(rfrExperiment);
+      experiment.Optimizers.Add(nnrExperiment);
+      experiment.Optimizers.Add(gpExperiment);
+
+      worker.ReportProgress(100);
+
       e.Result = experiment;
+    }
+
+    private void SetParameterRangeContraint(ParameterConfigurationTree pct, string parameterName, IRange rangeContraint) {
+      var pc = pct.AlgorithmConfiguration.ParameterConfigurations.Where(x => x.ParameterName == parameterName).SingleOrDefault();
+      pc.Optimize = true;
+      var vc = (RangeValueConfiguration)pc.ValueConfigurations.First();
+      vc.Optimize = true;
+      vc.RangeConstraint = rangeContraint;
+
+    }
+
+    private void WorkerProgressChanged(object sender, ProgressChangedEventArgs e) {
+      progressBar1.Value = e.ProgressPercentage;
     }