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source: branches/FitnessLandscapeAnalysis/HeuristicLab.Analysis.FitnessLandscape/Evolvability/EvolvabilityAnalyzer.cs @ 14927

Last change on this file since 14927 was 7176, checked in by gkronber, 13 years ago

#1696 adapted analyzers to compile with changes of r7172 (#1584)

File size: 8.0 KB
Line 
1using System;
2using System.Collections.Generic;
3using System.Drawing;
4using System.Linq;
5using HeuristicLab.Common;
6using HeuristicLab.Core;
7using HeuristicLab.Data;
8using HeuristicLab.Operators;
9using HeuristicLab.Optimization;
10using HeuristicLab.Optimization.Operators;
11using HeuristicLab.Parameters;
12using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
13
14namespace HeuristicLab.Analysis.FitnessLandscape.Evolvability {
15
16  [Item("Evolvability Analyzer", "Compares two sets of scopes containing the original population and the evolved population generating several evolvability measures.")]
17  public class EvolvabilityAnalyzer : AlgorithmOperator, IAnalyzer {
18
19    public bool EnabledByDefault {
20      get { return false; }
21    }
22
23    #region Parameters
24    public LookupParameter<DoubleValue> QualityParameter {
25      get { return (LookupParameter<DoubleValue>)Parameters["Quality"]; }
26    }
27    public LookupParameter<IntValue> SampleSizeParameter {
28      get { return (LookupParameter<IntValue>)Parameters["SampleSize"]; }
29    }
30    public LookupParameter<HeatMap> EvolvabilityPlotParameter {
31      get { return (LookupParameter<HeatMap>)Parameters["EvolvabilityPlot"]; }
32    }
33    public LookupParameter<VariableCollection> ResultsParameter {
34      get { return (LookupParameter<VariableCollection>)Parameters["Results"]; }
35    }
36    public LookupParameter<ScatterPlot> FitnessCloudParameter {
37      get { return (LookupParameter<ScatterPlot>)Parameters["FitnessCloud"]; }
38    }
39    public LookupParameter<DataTable> EvolvabilityPortraitParameter {
40      get { return (LookupParameter<DataTable>)Parameters["EvolvabilityPortrait"]; }
41    }
42    public ValueLookupParameter<IntValue> NrOfBinsParameter {
43      get { return (ValueLookupParameter<IntValue>)Parameters["NrOfBins"]; }
44    }
45    public ScopeParameter ScopeParameter {
46      get { return (ScopeParameter)Parameters["Scope"]; }
47    }
48    #endregion
49
50    #region Constructors
51    public EvolvabilityAnalyzer() {
52      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The qualities of evolved and unevolved solution candidates"));
53      Parameters.Add(new LookupParameter<IntValue>("SampleSize", "The number of samples or population size for evolvability analysis"));
54      Parameters.Add(new LookupParameter<HeatMap>("EvolvabilityPlot", "Collected evolvability values"));
55      Parameters.Add(new LookupParameter<VariableCollection>("Results", "The collection of all results of this algorithm"));
56      Parameters.Add(new LookupParameter<ScatterPlot>("FitnessCloud", "A fitness cloud of the original vs. evolved sample."));
57      Parameters.Add(new LookupParameter<DataTable>("EvolvabilityPortrait", "Evolvability Portrait as described in Smith et. al (2002). Evol Comput 10:1–34"));
58      Parameters.Add(new ValueLookupParameter<IntValue>("NrOfBins", "Nr of bins for evolvability portrait analysis", new IntValue(25)));
59      Parameters.Add(new ScopeParameter("Scope", "The current scope"));
60
61      var resultsCollector = new ResultsCollector();
62      resultsCollector.CollectedValues.Add(new LookupParameter<HeatMap>(EvolvabilityPlotParameter.Name));
63      resultsCollector.CollectedValues.Add(new LookupParameter<ScatterPlot>(FitnessCloudParameter.Name));
64      resultsCollector.CollectedValues.Add(new LookupParameter<DataTable>(EvolvabilityPortraitParameter.Name));
65
66      OperatorGraph.InitialOperator = resultsCollector;
67      resultsCollector.Successor = null;
68    }
69
70    [StorableConstructor]
71    protected EvolvabilityAnalyzer(bool deserializing) : base(deserializing) { }
72
73    protected EvolvabilityAnalyzer(EvolvabilityAnalyzer original, Cloner cloner)
74      : base(original, cloner) {
75    }
76    #endregion
77
78    public override IDeepCloneable Clone(Cloner cloner) {
79      return new EvolvabilityAnalyzer(this, cloner);
80    }
81
82    public override IOperation Apply() {
83      var points = GenerateQualityPairs(ScopeParameter.ActualValue);
84      GetOrCreateEvolvabilityPlot().Add(points);
85      GetOrCreateScatterPlot().Add(points);
86      CreateEvolvabilityPortrait(ScopeParameter.ActualValue);
87      return base.Apply();
88    }
89
90    private IEnumerable<PointF> GenerateQualityPairs(IScope globalScope) {
91      foreach (IScope sampleScope in globalScope.SubScopes) {
92        double originalQuality = ((DoubleValue)sampleScope.Variables[QualityParameter.ActualName].Value).Value;
93        foreach (IScope evolvedScope in sampleScope.SubScopes) {
94          double evolvedQuality = ((DoubleValue)evolvedScope.Variables[QualityParameter.ActualName].Value).Value;
95          yield return new PointF((float)originalQuality, (float)evolvedQuality);
96        }
97      }
98    }
99
100    private HeatMap GetOrCreateEvolvabilityPlot() {
101      HeatMap evolvabilityPlot = EvolvabilityPlotParameter.ActualValue as HeatMap;
102      if (evolvabilityPlot == null) {
103        evolvabilityPlot = new HeatMap();
104        evolvabilityPlot.Name = "Evolvability";
105        EvolvabilityPlotParameter.ActualValue = evolvabilityPlot;
106      }
107      return evolvabilityPlot;
108    }
109
110    private ScatterPlot GetOrCreateScatterPlot() {
111      ScatterPlot scatterPlot = FitnessCloudParameter.ActualValue as ScatterPlot;
112      if (scatterPlot == null) {
113        scatterPlot = new ScatterPlot();
114        scatterPlot.Name = "Fitness Cloud";
115        scatterPlot.XAxisName = "Parent Fitness";
116        scatterPlot.YAxisName = "Offspring Fitness";
117        FitnessCloudParameter.ActualValue = scatterPlot;
118      }
119      return scatterPlot;
120    }
121
122    private DataTable GetOrCreateEvolvabilityPortrait() {
123      DataTable evolvabilityPortrait = EvolvabilityPortraitParameter.ActualValue as DataTable;
124      if (evolvabilityPortrait == null) {
125        evolvabilityPortrait = new DataTable("Evolvability Portrait");
126        evolvabilityPortrait.Rows.Add(new DataRow("E_a", "Probability of non-deleterious mutation"));
127        evolvabilityPortrait.Rows.Add(new DataRow("E_b", "Average expected offspring fitness"));
128        evolvabilityPortrait.Rows.Add(new DataRow("E_c", "Top offspring fitness"));
129        evolvabilityPortrait.Rows.Add(new DataRow("E_d", "Bottom offspring fitness"));
130        EvolvabilityPortraitParameter.ActualValue = evolvabilityPortrait;
131      }
132      return evolvabilityPortrait;
133    }
134
135    private double GetQuality(IScope scope) {
136      return ((DoubleValue)scope.Variables[QualityParameter.ActualName].Value).Value;
137    }
138
139    private void CreateEvolvabilityPortrait(IScope globalScope) {
140      DataTable table = GetOrCreateEvolvabilityPortrait();
141      var e_a = table.Rows["E_a"].Values;
142      var e_b = table.Rows["E_b"].Values;
143      var e_c = table.Rows["E_c"].Values;
144      var e_d = table.Rows["E_d"].Values;
145      var qualities = globalScope.SubScopes.Select(s => GetQuality(s));
146      double min = qualities.Min();
147      double max = qualities.Max();
148      double range = max - min;
149      double stepSize = range / NrOfBinsParameter.ActualValue.Value;
150      double threshold = min + stepSize;
151
152      int n_not_worse = 0;
153      int n = 0;
154      double totalOffspringQuality = 0;
155      double best = double.MinValue;
156      double worst = double.MaxValue;
157      foreach (IScope sampleScope in globalScope.SubScopes) {
158        double originalQuality = GetQuality(sampleScope);
159        foreach (IScope evolvedScope in sampleScope.SubScopes) {
160          double evolvedQuality = GetQuality(evolvedScope);
161          if (evolvedQuality >= originalQuality)
162            n_not_worse++;
163          n++;
164          totalOffspringQuality += evolvedQuality;
165          best = Math.Max(best, evolvedQuality);
166          worst = Math.Min(worst, evolvedQuality);
167        }
168        if (originalQuality > threshold) {
169          threshold += stepSize;
170          e_a.Add(1.0 * n_not_worse / n);
171          e_b.Add(totalOffspringQuality / n);
172          e_c.Add(best);
173          e_d.Add(worst);
174          n_not_worse = 0;
175          n = 0;
176          totalOffspringQuality = 0;
177          best = double.MinValue;
178          worst = double.MaxValue;
179        }
180      }
181    }
182  }
183}
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