HeuristicLab.Algorithms.CMAEvolutionStrategy/3.4/CMAAnalyzer.cs

   1 #region License Information
   2 /* HeuristicLab
   3  * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
   4  *
   5  * This file is part of HeuristicLab.
   6  *
   7  * HeuristicLab is free software: you can redistribute it and/or modify
   8  * it under the terms of the GNU General Public License as published by
   9  * the Free Software Foundation, either version 3 of the License, or
  10  * (at your option) any later version.
  11  *
  12  * HeuristicLab is distributed in the hope that it will be useful,
  13  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  15  * GNU General Public License for more details.
  16  *
  17  * You should have received a copy of the GNU General Public License
  18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
  19  */
  20 #endregion
  21
  22 using System;
  23 using System.Linq;
  24 using HeuristicLab.Analysis;
  25 using HeuristicLab.Common;
  26 using HeuristicLab.Core;
  27 using HeuristicLab.Data;
  28 using HeuristicLab.Encodings.RealVectorEncoding;
  29 using HeuristicLab.Operators;
  30 using HeuristicLab.Optimization;
  31 using HeuristicLab.Parameters;
  32 using HEAL.Attic;
  33
  34 namespace HeuristicLab.Algorithms.CMAEvolutionStrategy {
  35   [Item("CMAAnalyzer", "Analyzes the development of strategy parameters and visualizes the performance of CMA-ES.")]
  36   [StorableType("7E36BAC4-D2A5-405D-B46F-FF91BC592D43")]
  37   public sealed class CMAAnalyzer : SingleSuccessorOperator, IAnalyzer, ISingleObjectiveOperator {
  38
  39     public bool EnabledByDefault {
  40       get { return false; }
  41     }
  42
  43     #region Parameter Properties
  44     public ILookupParameter<CMAParameters> StrategyParametersParameter {
  45       get { return (ILookupParameter<CMAParameters>)Parameters["StrategyParameters"]; }
  46     }
  47
  48     public ILookupParameter<RealVector> MeanParameter {
  49       get { return (ILookupParameter<RealVector>)Parameters["Mean"]; }
  50     }
  51
  52     public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
  53       get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
  54     }
  55
  56     public ILookupParameter<ResultCollection> ResultsParameter {
  57       get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
  58     }
  59     #endregion
  60
  61     [StorableConstructor]
  62     private CMAAnalyzer(StorableConstructorFlag _) : base(_) { }
  63     private CMAAnalyzer(CMAAnalyzer original, Cloner cloner) : base(original, cloner) { }
  64     public CMAAnalyzer()
  65       : base() {
  66       Parameters.Add(new LookupParameter<CMAParameters>("StrategyParameters", "The CMA strategy parameters to be analyzed."));
  67       Parameters.Add(new LookupParameter<RealVector>("Mean", "The mean real vector that is being optimized."));
  68       Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the solutions."));
  69       Parameters.Add(new LookupParameter<ResultCollection>("Results", "The collection to store the results in."));
  70     }
  71
  72     public override IDeepCloneable Clone(Cloner cloner) {
  73       return new CMAAnalyzer(this, cloner);
  74     }
  75
  76     public override IOperation Apply() {
  77       var sp = StrategyParametersParameter.ActualValue;
  78       var vector = MeanParameter.ActualValue;
  79       var results = ResultsParameter.ActualValue;
  80       var qualities = QualityParameter.ActualValue;
  81       double min = qualities[0].Value, max = qualities[0].Value, avg = qualities[0].Value;
  82       for (int i = 1; i < qualities.Length; i++) {
  83         if (qualities[i].Value < min) min = qualities[i].Value;
  84         if (qualities[i].Value > max) max = qualities[i].Value;
  85         avg += qualities[i].Value;
  86       }
  87       avg /= qualities.Length;
  88
  89       DataTable progress;
  90       if (results.ContainsKey("Progress")) {
  91         progress = (DataTable)results["Progress"].Value;
  92       } else {
  93         progress = new DataTable("Progress");
  94         progress.Rows.Add(new DataRow("AxisRatio"));
  95         progress.Rows.Add(new DataRow("Sigma"));
  96         progress.Rows.Add(new DataRow("Min Quality"));
  97         progress.Rows.Add(new DataRow("Max Quality"));
  98         progress.Rows.Add(new DataRow("Avg Quality"));
  99         progress.VisualProperties.YAxisLogScale = true;
 100         results.Add(new Result("Progress", progress));
 101       }
 102       progress.Rows["AxisRatio"].Values.Add(sp.AxisRatio);
 103       progress.Rows["Sigma"].Values.Add(sp.Sigma);
 104       progress.Rows["Min Quality"].Values.Add(min);
 105       progress.Rows["Max Quality"].Values.Add(max);
 106       progress.Rows["Avg Quality"].Values.Add(avg);
 107
 108       DataTable scaling;
 109       if (results.ContainsKey("Scaling")) {
 110         scaling = (DataTable)results["Scaling"].Value;
 111       } else {
 112         scaling = new DataTable("Scaling");
 113         scaling.VisualProperties.YAxisLogScale = true;
 114         for (int i = 0; i < sp.C.GetLength(0); i++)
 115           scaling.Rows.Add(new DataRow("Axis" + i.ToString()));
 116         results.Add(new Result("Scaling", scaling));
 117       }
 118       for (int i = 0; i < sp.C.GetLength(0); i++)
 119         scaling.Rows["Axis" + i.ToString()].Values.Add(sp.D[i]);
 120
 121       DataTable realVector;
 122       if (results.ContainsKey("Object Variables")) {
 123         realVector = (DataTable)results["Object Variables"].Value;
 124       } else {
 125         realVector = new DataTable("Object Variables");
 126         for (int i = 0; i < vector.Length; i++)
 127           realVector.Rows.Add(new DataRow("Axis" + i.ToString()));
 128         results.Add(new Result("Object Variables", realVector));
 129       }
 130       for (int i = 0; i < vector.Length; i++)
 131         realVector.Rows["Axis" + i.ToString()].Values.Add(vector[i]);
 132
 133       DataTable stdDevs;
 134       if (results.ContainsKey("Standard Deviations")) {
 135         stdDevs = (DataTable)results["Standard Deviations"].Value;
 136       } else {
 137         stdDevs = new DataTable("Standard Deviations");
 138         stdDevs.VisualProperties.YAxisLogScale = true;
 139         stdDevs.Rows.Add(new DataRow("MinStdDev"));
 140         stdDevs.Rows.Add(new DataRow("MaxStdDev"));
 141         for (int i = 0; i < vector.Length; i++)
 142           stdDevs.Rows.Add(new DataRow("Axis" + i.ToString()));
 143         results.Add(new Result("Standard Deviations", stdDevs));
 144       }
 145       for (int i = 0; i < vector.Length; i++)
 146         stdDevs.Rows["Axis" + i.ToString()].Values.Add(Math.Sqrt(sp.C[i, i]));
 147       stdDevs.Rows["MinStdDev"].Values.Add(sp.D.Min() * sp.Sigma);
 148       stdDevs.Rows["MaxStdDev"].Values.Add(sp.D.Max() * sp.Sigma);
 149
 150       return base.Apply();
 151     }
 152   }
 153 }