Changeset 10757

Timestamp:

04/17/14 15:34:25 (10 years ago)

Author:

gkronber

Message:

#1967 removed obsolete classes and cleaned up the implementation. Added the ProblemInstanceProvider interface to allow loading of CSV files.

Location:

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning

Files:

• . (modified) (1 prop)
• BestSolutionAnalyzer.cs (modified) (4 diffs)
• Evaluator.cs (modified) (4 diffs)
• GaussianProcessModelView.cs (deleted)
• GaussianProcessModelView.designer.cs (deleted)
• GaussianProcessRegressionModel.cs (deleted)
• GaussianProcessTuning.csproj (modified) (3 diffs)
• Grammar.cs (modified) (6 diffs)
• Interpreter.cs (modified) (2 diffs)
• Parameters.cs (deleted)
• Plugin.cs.frame (modified) (1 diff)
• Problem.cs (modified) (6 diffs)
• Properties (modified) (1 prop)
• Provider.cs (deleted)
• Solution.cs (deleted)
• SolutionTreeView.cs (deleted)

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning

@@ -2 +2 @@
 obj
 bin
+Plugin.cs

@@ -2 +2 @@
 obj
 bin
+Plugin.cs

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/BestSolutionAnalyzer.cs

@@ -17 +17 @@
     private const string QualityParameterName = "Quality";
     private const string SymbolicExpressionTreeParameterName = "GaussianProcessConfiguration";
-    private const string BestSolutionParameterName = "Best solution";
     private const string ResultsParameterName = "Results";
     private const string ProblemDataParameterName = "ProblemData";
     private const string InterpreterParameterName = "Interpreter";
     private const string SolutionParameterName = "Solution";
+    private const string TreeParameterName = "Covariance function";
 
     public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
@@ -31 +31 @@
     public IScopeTreeLookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
       get { return (IScopeTreeLookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
-    }
-    public ILookupParameter<Solution> BestSolutionParameter {
-      get { return (ILookupParameter<Solution>)Parameters[BestSolutionParameterName]; }
     }
     public ILookupParameter<ResultCollection> ResultParameter {
@@ -55 +52 @@
       Parameters.Add(new ScopeTreeLookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The Gaussian process configuration to evaluate represented as symbolic expression tree."));
       Parameters.Add(new ScopeTreeLookupParameter<IGaussianProcessSolution>(SolutionParameterName, "The solution of the Gaussian process algorithm."));
-      Parameters.Add(new LookupParameter<Solution>(BestSolutionParameterName, "The best Gaussian process configuration."));
       Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection of the algorithm."));
       Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, ""));
@@ -85 +81 @@
       }
       var cloner = new Cloner();
-      var problemData = cloner.Clone(ProblemDataParameter.ActualValue);
-      var interpreter = cloner.Clone(InterpreterParameter.ActualValue);
       bestTree = cloner.Clone(bestTree);
-      var bestModel = new GaussianProcessRegressionModel(bestTree, problemData, interpreter);
-      var bestSolutionConfiguration = new Solution(bestModel, problemData);
-
-      BestSolutionParameter.ActualValue = bestSolutionConfiguration;
 
       var resultCollection = ResultParameter.ActualValue;
-      if (!resultCollection.ContainsKey(BestSolutionParameterName)) {
-        resultCollection.Add(new Result(BestSolutionParameterName, "The best Gaussian process solution", bestSolutionConfiguration));
+      if (!resultCollection.ContainsKey(SolutionParameterName)) {
         resultCollection.Add(new Result(SolutionParameterName, "The Gaussian process algorithm solution", bestSolution));
+        resultCollection.Add(new Result(TreeParameterName, "The best covariance function", bestTree));
       } else {
-        resultCollection[BestSolutionParameterName].Value = bestSolutionConfiguration;
         resultCollection[SolutionParameterName].Value = bestSolution;
+        resultCollection[TreeParameterName].Value = bestTree;
       }
 

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/Evaluator.cs

@@ -20 +20 @@
     private const string ProblemDataParameterName = "ProblemData";
     private const string SolutionParameterName = "Solution";
+    private const string IterationsParameterName = "Iterations";
 
     public ILookupParameter<DoubleValue> QualityParameter {
@@ -36 +37 @@
       get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
     }
+    public ILookupParameter<IntValue> IterationsParameter {
+      get { return (ILookupParameter<IntValue>)Parameters[IterationsParameterName]; }
+    }
 
     [StorableConstructor]
@@ -46 +50 @@
       Parameters.Add(new LookupParameter<DoubleValue>(QualityParameterName, "The solution quality of the Gaussian process configuration."));
       Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(GaussianProcessConfigurationParameterName, "The Gaussian process configuration to evaluate represented as symbolic expression tree."));
+      Parameters.Add(new LookupParameter<IntValue>(IterationsParameterName, "Number of LM-BFGS iterations for hyper-parameter optimization."));
       Parameters.Add(new LookupParameter<Interpreter>(InterpreterParameterName, "The interpreter to use for evaluating Gaussian process configurations represented as trees."));
       Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "To regression problem data for which to learn a Gaussian process."));
@@ -63 +68 @@
       double quality;
       IGaussianProcessSolution solution;
-      InterpreterParameter.ActualValue.EvaluateGaussianProcessConfiguration(tree, ProblemDataParameter.ActualValue, out quality, out solution);
+      InterpreterParameter.ActualValue.EvaluateGaussianProcessConfiguration(tree, ProblemDataParameter.ActualValue, IterationsParameter.ActualValue.Value, out quality, out solution);
 
       if (double.IsInfinity(quality) || double.IsNaN(quality)) quality = 1E6; // a large value

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/GaussianProcessTuning.csproj

@@ -123 +123 @@
       <Private>False</Private>
     </Reference>
+    <Reference Include="HeuristicLab.Problems.Instances-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
+      <SpecificVersion>False</SpecificVersion>
+      <HintPath>..\..\..\trunk\sources\bin\HeuristicLab.Problems.Instances-3.3.dll</HintPath>
+    </Reference>
     <Reference Include="HeuristicLab.Random-3.3">
       <HintPath>..\..\..\trunk\sources\bin\HeuristicLab.Random-3.3.dll</HintPath>
@@ -141 +145 @@
   <ItemGroup>
     <Compile Include="BestSolutionAnalyzer.cs" />
-    <Compile Include="GaussianProcessRegressionModel.cs" />
     <Compile Include="Interpreter.cs">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="Parameters.cs" />
-    <Compile Include="Provider.cs" />
-    <Compile Include="Solution.cs" />
     <Compile Include="Grammar.cs" />
     <Compile Include="Problem.cs" />
@@ -159 +159 @@
     <None Include="Plugin.cs.frame" />
     <None Include="Properties\AssemblyInfo.cs.frame" />
-  </ItemGroup>
-  <ItemGroup>
-    <ProjectReference Include="..\HeuristicLab.Algorithms.DataAnalysis.Experimental\HeuristicLab.Algorithms.DataAnalysis.Experimental.csproj">
-      <Project>{6142B24E-9C86-410B-A65A-1DE65C8C2349}</Project>
-      <Name>HeuristicLab.Algorithms.DataAnalysis.Experimental</Name>
-    </ProjectReference>
   </ItemGroup>
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/Grammar.cs

@@ -42 +42 @@
     private void Initialize() {
       // create all symbols
+      var meanZero = new MeanZero();
       //var meanOne = new MeanOne();
-      var meanConst = new MeanConst();
-      var meanLinear = new MeanLinear(Dimension);
+      //var meanConst = new MeanConst();
+      //var meanLinear = new MeanLinear(Dimension);
       //var meanScale = new MeanScale();
-      var meanSum = new MeanSum();
-      var meanProd = new MeanProd();
+      //var meanSum = new MeanSum();
+      //var meanProd = new MeanProd();
       //var meanPow2 = new MeanPow(2) { Name = "MeanPow2" };
       //var meanPow3 = new MeanPow(3) { Name = "MeanPow3" };
@@ -70 +71 @@
       var covPeriodic1 = new CovPeriodic(1.0);
       covPeriodic1.Name = "CovPeriodic(1.0)";
-      var covPeriodicCO = new CovPeriodic(0.021817864467425927);
-      covPeriodicCO.Name = "CovPeriodicCO";
+      //var covPeriodicCO = new CovPeriodic(0.021817864467425927);
+      //covPeriodicCO.Name = "CovPeriodicCO";
       var covNoise = new CovNoise();
       var covScale = new CovScale();
@@ -83 +84 @@
       var meanSymbols = new List<ISymbol>()
                          {
-                            meanConst, 
-                            meanLinear,
-                            meanSum,  
-                            meanProd, 
+                           meanZero,
+                            //meanConst, 
+                            //meanLinear,
+                            //meanSum,  
+                            //meanProd, 
                             //meanPow2, 
                             //meanPow3, 
@@ -108 +110 @@
                             covPeriodic,
                             covPeriodic1,
-                            covPeriodicCO,
+                            //covPeriodicCO,
                             covNoise,
                             covScale, 
@@ -137 +139 @@
         AddAllowedChildSymbol(configurationStartSymbol, likSymbol, 2);
       }
-      foreach (var meanFunctionSymbol in new ISymbol[] { meanSum, meanProd }) {
-        foreach (var meanSymbol in meanSymbols) {
-          for (int i = 0; i < meanFunctionSymbol.MaximumArity; i++) {
-            AddAllowedChildSymbol(meanFunctionSymbol, meanSymbol, i);
-          }
-        }
-      }
+      //foreach (var meanFunctionSymbol in new ISymbol[] { meanSum, meanProd }) {
+      //  foreach (var meanSymbol in meanSymbols) {
+      //    for (int i = 0; i < meanFunctionSymbol.MaximumArity; i++) {
+      //      AddAllowedChildSymbol(meanFunctionSymbol, meanSymbol, i);
+      //    }
+      //  }
+      //}
 
       foreach (var covFunctionSymbol in new ISymbol[] { covSum, covProd, covScale }) {
@@ -171 +173 @@
                             covPeriodic,
                             covPeriodic1,
-                            covPeriodicCO,
+                            //covPeriodicCO,
                          }) {
         for (int i = 0; i < covMask.MaximumArity; i++) {

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/Interpreter.cs

@@ -26 +26 @@
     }
 
-    public void EvaluateGaussianProcessConfiguration(ISymbolicExpressionTree tree, IRegressionProblemData problemData, out double negLogLikelihood, out IGaussianProcessSolution solution) {
+    public void EvaluateGaussianProcessConfiguration(ISymbolicExpressionTree tree, IRegressionProblemData problemData, int iterations, out double negLogLikelihood, out IGaussianProcessSolution solution) {
       var meanFunction = GetMeanFunction(tree);
       var covFunction = GetCovFunction(tree);
@@ -36 +36 @@
       gprAlg.GaussianProcessModelCreatorParameter.Value =
         gprAlg.GaussianProcessModelCreatorParameter.ValidValues.First(
-          v => v is EigenGaussianProcessRegressionModelCreator);
-      gprAlg.MinimizationIterations = 50;
+          v => v is GaussianProcessRegressionModelCreator);
+      gprAlg.MinimizationIterations = iterations;
 
       var signal = new AutoResetEvent(false);

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/Plugin.cs.frame

@@ -17 +17 @@
   [PluginDependency("HeuristicLab.Persistence", "3.3")]
   [PluginDependency("HeuristicLab.Problems.DataAnalysis", "3.4")]
+  [PluginDependency("HeuristicLab.Problems.Instances", "3.3")]
   [PluginDependency("HeuristicLab.Random", "3.3")]
   [PluginDependency("HeuristicLab.Selection", "3.3")]

branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.GaussianProcessTuning/Problem.cs

@@ -10 +10 @@
 using HeuristicLab.PluginInfrastructure;
 using HeuristicLab.Problems.DataAnalysis;
+using HeuristicLab.Problems.Instances;
 
 namespace HeuristicLab.Problems.GaussianProcessTuning {
@@ -15 +16 @@
   [Creatable("Problems")]
   [Item("Gaussian Process Configuration Problem", "The Gaussian process configuration problem for genetic programming.")]
-  public class Problem : SingleObjectiveHeuristicOptimizationProblem<Evaluator, ISymbolicExpressionTreeCreator> {
+  public class Problem : SingleObjectiveHeuristicOptimizationProblem<Evaluator, ISymbolicExpressionTreeCreator>,
+    IProblemInstanceConsumer<IRegressionProblemData>, IProblemInstanceExporter<IRegressionProblemData> {
     private const string GaussianProcessConfigurationParameterName = "GaussianProcessConfiguration";
     private const string MaxGaussianProcessConfigurationLengthParameterName = "MaxConfigurationLength";
@@ -23 +25 @@
     private const string InterpreterParameterName = "Interpreter";
     private const string ProblemDataParameterName = "ProblemData";
+    private const string IterationsParameterName = "Iterations";
 
 
@@ -43 +46 @@
       get { return (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
     }
+    public IFixedValueParameter<IntValue> IterationsParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
+    }
 
     [StorableConstructor]
@@ -55 +61 @@
       : base(new Evaluator(), new ProbabilisticTreeCreator()) {
       Parameters.Add(new ValueParameter<Interpreter>(InterpreterParameterName, "The interpreter for Gaussian process configurations represented as trees.", new Interpreter()));
-      Parameters.Add(new FixedValueParameter<IntValue>(DimensionParameterName, "The dimension parameter for the Gaussian process configuration.", new IntValue(10)));
-      Parameters.Add(new FixedValueParameter<IntValue>(MaxGaussianProcessConfigurationParameterName, "Maximal depth of the Gaussian process configuration.", new IntValue(6)));
-      Parameters.Add(new FixedValueParameter<IntValue>(MaxGaussianProcessConfigurationLengthParameterName, "Maximal length of the Gaussian process configuration.", new IntValue(15)));
+      Parameters.Add(new FixedValueParameter<IntValue>(DimensionParameterName, "The dimension parameter for the Gaussian process configuration.", new IntValue(1)));
+      Parameters.Add(new FixedValueParameter<IntValue>(MaxGaussianProcessConfigurationParameterName, "Maximal depth of the Gaussian process configuration.", new IntValue(7)));
+      Parameters.Add(new FixedValueParameter<IntValue>(MaxGaussianProcessConfigurationLengthParameterName, "Maximal length of the Gaussian process configuration.", new IntValue(25)));
       Parameters.Add(new ValueParameter<Grammar>(GaussianProcessConfigurationGrammarParameterName, "Grammar for the Gaussian process configuration.",
                      new Grammar()));
       Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data to learn a Gaussian process for.", new RegressionProblemData()));
+      Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "The number of LM-BFGS iterations for hyper-parameter tuning.", new IntValue(50)));
       Maximization.Value = false;
       InitializeOperators();
@@ -167 +174 @@
       }
     }
+
+    public void Load(IRegressionProblemData data) {
+      Name = data.Name;
+      Description = data.Description;
+      ProblemDataParameter.Value = data;
+    }
+
+    public IRegressionProblemData Export() {
+      return ProblemDataParameter.Value;
+    }
   }
 }

@@ -2 +2 @@
 obj
 bin
+Plugin.cs