Changeset 15064 for branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/InfillCriteria/AugmentedExpectedImprovement.cs

Timestamp:

06/26/17 09:10:56 (7 years ago)

Author:

bwerth

Message:

#2745 implemented EGO as EngineAlgorithm + some simplifications in the IInfillCriterion interface

File:

• branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/InfillCriteria/AugmentedExpectedImprovement.cs (modified) (2 diffs)

branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/InfillCriteria/AugmentedExpectedImprovement.cs

@@ -35 +35 @@
   [StorableClass]
   [Item("AugmentedExpectedImprovement", "Noisy InfillCriterion, Extension of the Expected Improvement as described in\n Global optimization of stochastic black-box systems via sequential kriging meta-models.\r\nHuang, D., Allen, T., Notz, W., Zeng, N.")]
-  public class AugmentedExpectedImprovement : ExpectedImprovement {
-
-
+  public class AugmentedExpectedImprovement : ExpectedImprovementBase {
 
     #region Parameternames
-
     public const string AlphaParameterName = "Alpha";
-
     #endregion
 
     #region Parameters
-
     public IValueParameter<DoubleValue> AlphaParameter => Parameters[AlphaParameterName] as IValueParameter<DoubleValue>;
-
     #endregion
 
     #region Properties
-
     public double Alpha => AlphaParameter.Value.Value;
     [Storable]
     private double Tau;
-
     #endregion
 
-
-    #region HL-Constructors, Serialization and Cloning 
+    #region Constructors, Serialization and Cloning 
     [StorableConstructor]
-    private AugmentedExpectedImprovement(bool deserializing) : base(deserializing) { }
-
-    private AugmentedExpectedImprovement(AugmentedExpectedImprovement original, Cloner cloner) : base(original, cloner) {
+    protected AugmentedExpectedImprovement(bool deserializing) : base(deserializing) { }
+    protected AugmentedExpectedImprovement(AugmentedExpectedImprovement original, Cloner cloner) : base(original, cloner) {
       Tau = original.Tau;
     }
-
     public AugmentedExpectedImprovement() {
       Parameters.Add(new ValueParameter<DoubleValue>(AlphaParameterName, "The Alpha value specifiying the robustness of the \"effective best solution\". Recommended value is 1", new DoubleValue(1.0)));
-
     }
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -82 +70 @@
     }
 
-    protected override void Initialize() {
-      if (ExpensiveMaximization) throw new NotImplementedException("AugmentedExpectedImprovement for maximization not yet implemented");
-      var solution = RegressionSolution as IConfidenceRegressionSolution;
-      if (solution == null) throw new ArgumentException("can not calculate Augmented EI without a regression solution providing confidence values");
+    protected override double Evaluate(RealVector vector, double estimatedFitness, double estimatedStandardDeviation) {
+      var d = GetEstimatedImprovement(BestFitness, estimatedFitness, estimatedStandardDeviation, ExploitationWeight, ExpensiveMaximization);
+      return d * (1 - Tau / Math.Sqrt(estimatedStandardDeviation * estimatedStandardDeviation + Tau * Tau));
+    }
 
+    protected override double FindBestFitness(IConfidenceRegressionSolution solution) {
       Tau = RegressionSolution.EstimatedTrainingValues.Zip(RegressionSolution.ProblemData.TargetVariableTrainingValues, (d, d1) => Math.Abs(d - d1)).Average();
-      var xss = new RealVector(Encoding.Length);
+      var bestSolution = new RealVector(Encoding.Length);
       var xssIndex = solution.EstimatedTrainingValues.Zip(solution.EstimatedTrainingValues, (m, s2) => m + Alpha * Math.Sqrt(s2)).ArgMin(x => x);
       var i = solution.ProblemData.TrainingIndices.ToArray()[xssIndex];
-      for (var j = 0; j < Encoding.Length; j++) xss[j] = solution.ProblemData.Dataset.GetDoubleValue(i, j);
-
-      YMin = RegressionSolution.Model.GetEstimation(xss);
+      for (var j = 0; j < Encoding.Length; j++) bestSolution[j] = solution.ProblemData.Dataset.GetDoubleValue(i, j);
+      return RegressionSolution.Model.GetEstimation(bestSolution);
     }
   }