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source: branches/HeuristicLab.EvolutionTracking/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisGenealogyAnalyzer.cs @ 16099

Last change on this file since 16099 was 12892, checked in by bburlacu, 9 years ago

#1772: Made GenealogyAnalyzer class abstract and added abstract method EvaluateIntermediateChildren in order to assign qualities to intermediate vertices in the genealogy graph. Implemented SymbolicDataAnalysisGeneticOperatorImprovementAnalyzer which shows parent-child quality statistics for crossover and mutation.

File size: 5.4 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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
22using System.Linq;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.EvolutionTracking;
28using HeuristicLab.Optimization;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
33  [Item("SymbolicDataAnalysisGenealogyAnalyzer", "Genealogy analyzer for symbolic data analysis problems")]
34  [StorableClass]
35  public class SymbolicDataAnalysisGenealogyAnalyzer : GenealogyAnalyzer<ISymbolicExpressionTree> {
36    private const string EvaluatorParameterName = "Evaluator";
37    private const string ProblemDataParameterName = "ProblemData";
38    private const string InterpreterParameterName = "SymbolicExpressionTreeInterpreter";
39    private const string EstimationLimitsParameterName = "EstimationLimits";
40    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
41
42    #region parameters
43    public ILookupParameter<ISingleObjectiveEvaluator> EvaluatorParameter {
44      get {
45        return (ILookupParameter<ISingleObjectiveEvaluator>)Parameters[EvaluatorParameterName];
46      }
47    }
48
49    public ILookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
50      get {
51        return (ILookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName];
52      }
53    }
54
55    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter {
56      get {
57        return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName];
58      }
59    }
60
61    public ILookupParameter<DoubleLimit> EstimationLimitsParameter {
62      get { return (ILookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
63    }
64
65    public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
66      get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
67    }
68    #endregion
69
70    public SymbolicDataAnalysisGenealogyAnalyzer() {
71      Parameters.Add(new LookupParameter<ISingleObjectiveEvaluator>(EvaluatorParameterName));
72      Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName));
73      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(InterpreterParameterName));
74      Parameters.Add(new LookupParameter<DoubleLimit>(EstimationLimitsParameterName));
75      Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName));
76    }
77
78    public SymbolicDataAnalysisGenealogyAnalyzer(SymbolicDataAnalysisGenealogyAnalyzer original, Cloner cloner)
79      : base(original, cloner) {
80    }
81
82    public override IDeepCloneable Clone(Cloner cloner) {
83      return new SymbolicDataAnalysisGenealogyAnalyzer(this, cloner);
84    }
85
86    [StorableConstructor]
87    protected SymbolicDataAnalysisGenealogyAnalyzer(bool deserializing) : base(deserializing) {
88    }
89
90    protected override void EvaluateIntermediateChildren() {
91      var results = ResultsParameter.ActualValue;
92      var graph = (IGenealogyGraph<ISymbolicExpressionTree>)results["PopulationGraph"].Value;
93      var population = PopulationParameter.ActualValue;
94      var generation = GenerationsParameter.ActualValue.Value;
95      var problemData = ProblemDataParameter.ActualValue;
96
97      var vertices = population.Select(graph.GetByContent).Where(x => x.InDegree == 1).Select(x => x.Parents.First());
98      var intermediateVertices = vertices.Where(x => x.Rank.IsAlmost(generation - 0.5));
99
100      var classificationProblemData = problemData as IClassificationProblemData;
101      var regressionProblemData = problemData as IRegressionProblemData;
102      if (classificationProblemData != null) {
103        var evaluator = (ISymbolicDataAnalysisSingleObjectiveEvaluator<IClassificationProblemData>)EvaluatorParameter.ActualValue;
104        foreach (var v in intermediateVertices) {
105          var child = v.Data;
106          v.Quality = evaluator.Evaluate(this.ExecutionContext, child, classificationProblemData, classificationProblemData.TrainingIndices);
107        }
108      } else if (regressionProblemData != null) {
109        var evaluator = (ISymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>)EvaluatorParameter.ActualValue;
110        foreach (var v in intermediateVertices) {
111          var child = v.Data;
112          v.Quality = evaluator.Evaluate(this.ExecutionContext, child, regressionProblemData, problemData.TrainingIndices);
113        }
114      }
115    }
116  }
117}
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