source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs @ 7521

Last change on this file since 7521 was 7521, checked in by mkommend, 8 years ago

#1682: Forbid name changes in new gp crossovers

File size: 6.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
32  [Item("SemanticSimilarityCrossover", "An operator which performs subtree swapping based on the notion semantic similarity between subtrees\n" +
33                                       "(criteria: mean of the absolute differences between the estimated output values of the two subtrees, falling into a user-defined range)\n" +
34                                       "- Take two parent individuals P0 and P1\n" +
35                                       "- Randomly choose a node N from the P0\n" +
36                                       "- Find the first node M that satisfies the semantic similarity criteria\n" +
37                                       "- Swap N for M and return P0")]
38  public sealed class SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
39    private const string SemanticSimilarityRangeParameterName = "SemanticSimilarityRange";
40
41    #region Parameter properties
42    public IValueParameter<DoubleRange> SemanticSimilarityRangeParameter {
43      get { return (IValueParameter<DoubleRange>)Parameters[SemanticSimilarityRangeParameterName]; }
44    }
45    #endregion
46
47    #region Properties
48    public DoubleRange SemanticSimilarityRange {
49      get { return SemanticSimilarityRangeParameter.Value; }
50    }
51    #endregion
52
53    [StorableConstructor]
54    private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(bool deserializing) : base(deserializing) { }
55    private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner) : base(original, cloner) { }
56    public SymbolicDataAnalysisExpressionSemanticSimilarityCrossover()
57      : base() {
58      Parameters.Add(new ValueLookupParameter<DoubleRange>(SemanticSimilarityRangeParameterName, "Semantic similarity interval.", new DoubleRange(0.0001, 10)));
59      name = "SemanticSimilarityCrossover";
60    }
61    public override IDeepCloneable Clone(Cloner cloner) {
62      return new SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T>(this, cloner);
63    }
64
65    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
66      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
67      List<int> rows = GenerateRowsToEvaluate().ToList();
68      T problemData = ProblemDataParameter.ActualValue;
69      return Cross(random, parent0, parent1, interpreter, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value, SemanticSimilarityRange);
70    }
71
72    /// <summary>
73    /// Takes two parent individuals P0 and P1.
74    /// Randomly choose a node i from the first parent, then get a node j from the second parent that matches the semantic similarity criteria.
75    /// </summary>
76    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
77                                                T problemData, List<int> rows, int maxDepth, int maxLength, DoubleRange range) {
78      var crossoverPoints0 = new List<CutPoint>();
79      parent0.Root.ForEachNodePostfix((n) => {
80        if (n.Parent != null && n.Parent != parent0.Root)
81          crossoverPoints0.Add(new CutPoint(n.Parent, n));
82      });
83      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
84      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
85      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
86
87      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
88      parent1.Root.ForEachNodePostfix((n) => {
89        if (n.Parent != null && n.Parent != parent1.Root) {
90          if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
91            allowedBranches.Add(n);
92        }
93      });
94
95      if (allowedBranches.Count == 0)
96        return parent0;
97
98      var dataset = problemData.Dataset;
99
100      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
101      var rootSymbol = new ProgramRootSymbol();
102      var startSymbol = new StartSymbol();
103      var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol);
104      List<double> estimatedValues0 = interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows).ToList();
105      crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore parent
106      ISymbolicExpressionTreeNode selectedBranch = null;
107
108      // pick the first node that fulfills the semantic similarity conditions
109      foreach (var node in allowedBranches) {
110        var parent = node.Parent;
111        var tree1 = CreateTreeFromNode(random, node, startSymbol, rootSymbol); // this will affect node.Parent
112        List<double> estimatedValues1 = interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows).ToList();
113        node.Parent = parent; // restore parent
114
115        OnlineCalculatorError errorState;
116        double ssd = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedValues0, estimatedValues1, out errorState);
117
118        if (range.Start <= ssd && ssd <= range.End) {
119          selectedBranch = node;
120          break;
121        }
122      }
123
124      // perform the actual swap
125      if (selectedBranch != null)
126        Swap(crossoverPoint0, selectedBranch);
127      return parent0;
128    }
129  }
130}
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