Changeset 15064 for branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/InfillCriteria/ExpectedQuantileImprovement.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/ExpectedQuantileImprovement.cs (modified) (3 diffs)

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

@@ -35 +35 @@
   [StorableClass]
   [Item("ExpectedQuantileImprovement", "Noisy InfillCriterion, Extension of the Expected Improvement as described in \n Noisy expectedimprovement and on - line computation time allocation for the optimization of simulators with tunable fidelitys\r\nPicheny, V., Ginsbourger, D., Richet, Y")]
-  public class ExpectedQuantileImprovement : ExpectedImprovement {
+  public class ExpectedQuantileImprovement : ExpectedImprovementBase {
 
     #region Parameternames
@@ -48 +48 @@
 
     #region Properties
-
     public int MaxEvaluations => MaxEvaluationsParameter.Value.Value;
     public double Alpha => AlphaParameter.Value.Value;
     [Storable]
     private double Tau;
-
     #endregion
 
     #region HL-Constructors, Serialization and Cloning 
     [StorableConstructor]
-    private ExpectedQuantileImprovement(bool deserializing) : base(deserializing) { }
-
-    private ExpectedQuantileImprovement(ExpectedQuantileImprovement original, Cloner cloner) : base(original, cloner) {
+    protected ExpectedQuantileImprovement(bool deserializing) : base(deserializing) { }
+    protected ExpectedQuantileImprovement(ExpectedQuantileImprovement original, Cloner cloner) : base(original, cloner) {
       Tau = original.Tau;
     }
-
     public ExpectedQuantileImprovement() {
       Parameters.Add(new FixedValueParameter<DoubleValue>(AlphaParameterName, "The Alpha value specifiying the robustness of the \"effective best solution\". Recommended value is 1.0", new DoubleValue(1.0)));
-      Parameters.Add(new ValueParameter<IntValue>(MaxEvaluationsParameterName, "The maximum number of evaluations allowed for EGO", new IntValue(100)));
+      Parameters.Add(new ValueParameter<IntValue>(MaxEvaluationsParameterName, "The maximum number of evaluations allowed for EGO", new IntValue(500)));
       MaxEvaluationsParameter.Hidden = true;
     }
@@ -74 +70 @@
     #endregion
 
-    public override double Evaluate(RealVector vector) {
-      var model = RegressionSolution.Model as IConfidenceRegressionModel;
-      var s2 = model.GetVariance(vector);
+    protected override double FindBestFitness(IConfidenceRegressionSolution solution) {
+      Tau = RegressionSolution.EstimatedTrainingValues.Zip(solution.ProblemData.TargetVariableTrainingValues, (d, d1) => Math.Abs(d - d1)).Average();
+      Tau = Tau * Tau / (MaxEvaluations - solution.ProblemData.Dataset.Rows % MaxEvaluations + 1);
 
-      var yhat = model.GetEstimation(vector) + Alpha * Math.Sqrt(Tau * s2 / (Tau + s2));
-      var s = Math.Sqrt(s2 * s2 / (Tau + s2));
+      var index = solution.EstimatedTrainingValues.Zip(solution.EstimatedTrainingVariances, (m, s2) => m + Alpha * Math.Sqrt(s2)).ArgMin(x => x);
+      return solution.EstimatedTrainingValues.ToArray()[index];
 
-      return GetEstimatedImprovement(YMin, yhat, s, ExploitationWeight);
     }
 
-    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");
-
-      Tau = RegressionSolution.EstimatedTrainingValues.Zip(RegressionSolution.ProblemData.TargetVariableTrainingValues, (d, d1) => Math.Abs(d - d1)).Average();
-      Tau = Tau * Tau / (MaxEvaluations - RegressionSolution.ProblemData.Dataset.Rows + 1);
-
-      var xss = new RealVector(Encoding.Length);
-      var xssIndex = solution.EstimatedTrainingVariances.Zip(solution.EstimatedTrainingVariances, (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);
+    protected override double Evaluate(RealVector vector, double estimatedFitness, double estimatedStandardDeviation) {
+      var s2 = estimatedStandardDeviation * estimatedStandardDeviation;
+      var penalty = Alpha * Math.Sqrt(Tau * s2 / (Tau + s2));
+      var yhat = estimatedFitness + (ExpensiveMaximization ? -penalty : penalty);
+      var s = Math.Sqrt(s2 * s2 / (Tau + s2));
+      return GetEstimatedImprovement(BestFitness, yhat, s, ExploitationWeight, ExpensiveMaximization);
     }