source: branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 8 months ago

#2520: updated year of copyrights

File size: 6.7 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Random;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
33  [Item("SemanticSimilarityCrossover", "An operator which performs subtree swapping based on the notion semantic similarity between subtrees\n" +
34                                       "(criteria: mean of the absolute differences between the estimated output values of the two subtrees, falling into a user-defined range)\n" +
35                                       "- Take two parent individuals P0 and P1\n" +
36                                       "- Randomly choose a node N from the P0\n" +
37                                       "- Find the first node M that satisfies the semantic similarity criteria\n" +
38                                       "- Swap N for M and return P0")]
39  [StorableClass]
40  public sealed class SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
41    private const string SemanticSimilarityRangeParameterName = "SemanticSimilarityRange";
42
43    #region Parameter properties
44    public IValueParameter<DoubleRange> SemanticSimilarityRangeParameter {
45      get { return (IValueParameter<DoubleRange>)Parameters[SemanticSimilarityRangeParameterName]; }
46    }
47    #endregion
48
49    #region Properties
50    public DoubleRange SemanticSimilarityRange {
51      get { return SemanticSimilarityRangeParameter.Value; }
52    }
53    #endregion
54
55    [StorableConstructor]
56    private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(bool deserializing) : base(deserializing) { }
57    private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner) : base(original, cloner) { }
58    public SymbolicDataAnalysisExpressionSemanticSimilarityCrossover()
59      : base() {
60      Parameters.Add(new ValueLookupParameter<DoubleRange>(SemanticSimilarityRangeParameterName, "Semantic similarity interval.", new DoubleRange(0.0001, 10)));
61      name = "SemanticSimilarityCrossover";
62    }
63    public override IDeepCloneable Clone(Cloner cloner) {
64      return new SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T>(this, cloner);
65    }
66
67    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
68      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
69      List<int> rows = GenerateRowsToEvaluate().ToList();
70      T problemData = ProblemDataParameter.ActualValue;
71      return Cross(random, parent0, parent1, interpreter, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value, SemanticSimilarityRange);
72    }
73
74    /// <summary>
75    /// Takes two parent individuals P0 and P1.
76    /// Randomly choose a node i from the first parent, then get a node j from the second parent that matches the semantic similarity criteria.
77    /// </summary>
78    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
79                                                T problemData, List<int> rows, int maxDepth, int maxLength, DoubleRange range) {
80      var crossoverPoints0 = new List<CutPoint>();
81      parent0.Root.ForEachNodePostfix((n) => {
82        if (n.Parent != null && n.Parent != parent0.Root)
83          crossoverPoints0.Add(new CutPoint(n.Parent, n));
84      });
85
86      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
87      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
88      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
89
90      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
91      parent1.Root.ForEachNodePostfix((n) => {
92        if (n.Parent != null && n.Parent != parent1.Root) {
93          if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
94            allowedBranches.Add(n);
95        }
96      });
97
98      if (allowedBranches.Count == 0)
99        return parent0;
100
101      var dataset = problemData.Dataset;
102
103      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
104      var rootSymbol = new ProgramRootSymbol();
105      var startSymbol = new StartSymbol();
106      var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol);
107      List<double> estimatedValues0 = interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows).ToList();
108      crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore parent
109      ISymbolicExpressionTreeNode selectedBranch = null;
110
111      // pick the first node that fulfills the semantic similarity conditions
112      foreach (var node in allowedBranches) {
113        var parent = node.Parent;
114        var tree1 = CreateTreeFromNode(random, node, startSymbol, rootSymbol); // this will affect node.Parent
115        List<double> estimatedValues1 = interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows).ToList();
116        node.Parent = parent; // restore parent
117
118        OnlineCalculatorError errorState;
119        double ssd = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedValues0, estimatedValues1, out errorState);
120
121        if (range.Start <= ssd && ssd <= range.End) {
122          selectedBranch = node;
123          break;
124        }
125      }
126
127      // perform the actual swap
128      if (selectedBranch != null)
129        Swap(crossoverPoint0, selectedBranch);
130      return parent0;
131    }
132  }
133}
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