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Ignore:
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:
1 edited

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  • branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/InfillCriteria/ExpectedImprovement.cs

    r14818 r15064  
    2424using HeuristicLab.Common;
    2525using HeuristicLab.Core;
    26 using HeuristicLab.Data;
    2726using HeuristicLab.Encodings.RealVectorEncoding;
    28 using HeuristicLab.Parameters;
    2927using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    3028using HeuristicLab.Problems.DataAnalysis;
    … …  
    3533  [StorableClass]
    3634  [Item("ExpectedImprovementMeassure", "Extension of the Expected Improvement to a weighted version by ANDRAS SÓBESTER , STEPHEN J. LEARY and ANDY J. KEANE   in \n On the Design of Optimization Strategies Based on Global Response Surface Approximation Models")]
    37   public class ExpectedImprovement : InfillCriterionBase {
    38 
    39     #region ParameterNames
    40     private const string ExploitationWeightParameterName = "ExploitationWeight";
    41     #endregion
    42 
    43     #region ParameterProperties
    44     public IFixedValueParameter<DoubleValue> ExploitationWeightParameter => Parameters[ExploitationWeightParameterName] as IFixedValueParameter<DoubleValue>;
    45 
    46     #endregion
    47 
    48     #region Properties
    49     protected double ExploitationWeight => ExploitationWeightParameter.Value.Value;
    50 
    51     [Storable]
    52     protected double YMin;
    53     #endregion
    54 
    55     #region HL-Constructors, Serialization and Cloning
     35  public sealed class ExpectedImprovement : ExpectedImprovementBase {
     36    #region Constructors, Serialization and Cloning
    5637    [StorableConstructor]
    57     protected ExpectedImprovement(bool deserializing) : base(deserializing) { }
    58     [StorableHook(HookType.AfterDeserialization)]
    59     private void AfterDeserialization() {
    60       RegisterEventhandlers();
    61     }
    62     protected ExpectedImprovement(ExpectedImprovement original, Cloner cloner) : base(original, cloner) {
    63       RegisterEventhandlers();
    64     }
    65     public ExpectedImprovement() {
    66       Parameters.Add(new FixedValueParameter<DoubleValue>(ExploitationWeightParameterName, "A value between 0 and 1 indicating the focus on exploration (0) or exploitation (1)", new DoubleValue(0.5)));
    67       RegisterEventhandlers();
    68     }
     38    private ExpectedImprovement(bool deserializing) : base(deserializing) { }
     39    private ExpectedImprovement(ExpectedImprovement original, Cloner cloner) : base(original, cloner) { }
     40    public ExpectedImprovement() { }
    6941    public override IDeepCloneable Clone(Cloner cloner) {
    7042      return new ExpectedImprovement(this, cloner);
    … …  
    7648      var yhat = model.GetEstimation(vector);
    7749      var s = Math.Sqrt(model.GetVariance(vector));
    78       return GetEstimatedImprovement(YMin, yhat, s, ExploitationWeight);
     50      return GetEstimatedImprovement(BestFitness, yhat, s, ExploitationWeight, ExpensiveMaximization);
    7951    }
    8052
    81     public override bool Maximization() {
    82       return true;
     53    protected override double Evaluate(RealVector vector, double estimatedFitness, double estimatedStandardDeviation) {
     54      return GetEstimatedImprovement(BestFitness, estimatedFitness, estimatedStandardDeviation, ExploitationWeight, ExpensiveMaximization);
    8355    }
    8456
    85     protected override void Initialize() {
    86       if (ExpensiveMaximization) throw new NotImplementedException("Expected Improvement for maximization not yet implemented");
    87       var model = RegressionSolution.Model as IConfidenceRegressionModel;
    88       if (model == null) throw new ArgumentException("can not calculate EI without confidence measure");
    89       YMin = RegressionSolution.ProblemData.TargetVariableTrainingValues.Min();
     57    protected override double FindBestFitness(IConfidenceRegressionSolution solution) {
     58      return ExpensiveMaximization ? solution.ProblemData.TargetVariableTrainingValues.Max() : solution.ProblemData.TargetVariableTrainingValues.Min();
    9059    }
    91 
    92     #region Eventhandling
    93     private void RegisterEventhandlers() {
    94       DeregisterEventhandlers();
    95       ExploitationWeightParameter.Value.ValueChanged += OnExploitationWeightChanged;
    96     }
    97     private void DeregisterEventhandlers() {
    98       ExploitationWeightParameter.Value.ValueChanged -= OnExploitationWeightChanged;
    99     }
    100     private void OnExploitationWeightChanged(object sender, EventArgs e) {
    101       ExploitationWeightParameter.Value.ValueChanged -= OnExploitationWeightChanged;
    102       ExploitationWeightParameter.Value.Value = Math.Max(0, Math.Min(ExploitationWeight, 1));
    103       ExploitationWeightParameter.Value.ValueChanged += OnExploitationWeightChanged;
    104     }
    105     #endregion
    106 
    107     #region Helpers
    108     protected static double GetEstimatedImprovement(double ymin, double yhat, double s, double w) {
    109       if (Math.Abs(s) < double.Epsilon) return 0;
    110       var val = (ymin - yhat) / s;
    111       var res = w * (ymin - yhat) * StandardNormalDistribution(val) + (1 - w) * s * StandardNormalDensity(val);
    112       return double.IsInfinity(res) || double.IsNaN(res) ? 0 : res;
    113     }
    114 
    115     private static double StandardNormalDensity(double x) {
    116       if (Math.Abs(x) > 10) return 0;
    117       return Math.Exp(-0.5 * x * x) / Math.Sqrt(2 * Math.PI);
    118     }
    119 
    120     //taken from https://www.johndcook.com/blog/2009/01/19/stand-alone-error-function-erf/
    121     private static double StandardNormalDistribution(double x) {
    122       if (x > 10) return 1;
    123       if (x < -10) return 0;
    124       const double a1 = 0.254829592;
    125       const double a2 = -0.284496736;
    126       const double a3 = 1.421413741;
    127       const double a4 = -1.453152027;
    128       const double a5 = 1.061405429;
    129       const double p = 0.3275911;
    130       var sign = x < 0 ? -1 : 1;
    131       x = Math.Abs(x) / Math.Sqrt(2.0);
    132       var t = 1.0 / (1.0 + p * x);
    133       var y = 1.0 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.Exp(-x * x);
    134       return 0.5 * (1.0 + sign * y);
    135     }
    136     #endregion
    13760  }
    13861}
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