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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs @ 13371

Last change on this file since 13371 was 12744, checked in by gkronber, 9 years ago

#2359: added after-deserialization code for backwards-compatibility

File size: 5.7 KB
Line 
1#region License Information
2
3/* HeuristicLab
4 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21
22#endregion
23
24using System.Collections.Generic;
25using System.Linq;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
33  [StorableClass]
34  [Item("SymbolicRegressionPruningOperator", "An operator which prunes symbolic regression trees.")]
35  public class SymbolicRegressionPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
36    private const string EvaluatorParameterName = "Evaluator";
37
38    #region parameter properties
39    public ILookupParameter<ISymbolicRegressionSingleObjectiveEvaluator> EvaluatorParameter {
40      get { return (ILookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>)Parameters[EvaluatorParameterName]; }
41    }
42    #endregion
43
44    protected SymbolicRegressionPruningOperator(SymbolicRegressionPruningOperator original, Cloner cloner)
45      : base(original, cloner) {
46    }
47    public override IDeepCloneable Clone(Cloner cloner) {
48      return new SymbolicRegressionPruningOperator(this, cloner);
49    }
50
51    [StorableConstructor]
52    protected SymbolicRegressionPruningOperator(bool deserializing) : base(deserializing) { }
53
54    public SymbolicRegressionPruningOperator(ISymbolicDataAnalysisSolutionImpactValuesCalculator impactValuesCalculator)
55      : base(impactValuesCalculator) {
56      Parameters.Add(new LookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>(EvaluatorParameterName));
57    }
58
59    [StorableHook(HookType.AfterDeserialization)]
60    private void AfterDeserialization() {
61      // BackwardsCompatibility3.3
62      #region Backwards compatible code, remove with 3.4
63      base.ImpactValuesCalculator = new SymbolicRegressionSolutionImpactValuesCalculator();
64      if (!Parameters.ContainsKey(EvaluatorParameterName)) {
65        Parameters.Add(new LookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>(EvaluatorParameterName));
66      }
67      #endregion
68    }
69
70    protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
71      return new SymbolicRegressionModel(tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
72    }
73
74    protected override double Evaluate(IDataAnalysisModel model) {
75      var regressionModel = (ISymbolicRegressionModel)model;
76      var regressionProblemData = (IRegressionProblemData)ProblemDataParameter.ActualValue;
77      var evaluator = EvaluatorParameter.ActualValue;
78      var fitnessEvaluationPartition = FitnessCalculationPartitionParameter.ActualValue;
79      var rows = Enumerable.Range(fitnessEvaluationPartition.Start, fitnessEvaluationPartition.Size);
80      return evaluator.Evaluate(this.ExecutionContext, regressionModel.SymbolicExpressionTree, regressionProblemData, rows);
81    }
82
83    public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, SymbolicRegressionSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IRegressionProblemData problemData, DoubleLimit estimationLimits, IEnumerable<int> rows, double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false) {
84      var clonedTree = (ISymbolicExpressionTree)tree.Clone();
85      var model = new SymbolicRegressionModel(clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
86      var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList(); // skip the nodes corresponding to the ProgramRootSymbol and the StartSymbol
87
88      double qualityForImpactsCalculation = double.NaN; // pass a NaN value initially so the impact calculator will calculate the quality
89
90      for (int i = 0; i < nodes.Count; ++i) {
91        var node = nodes[i];
92        if (node is ConstantTreeNode) continue;
93
94        double impactValue, replacementValue;
95        double newQualityForImpactsCalculation;
96        impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation);
97
98        if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
99        if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold) continue;
100
101        var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
102        constantNode.Value = replacementValue;
103
104        ReplaceWithConstant(node, constantNode);
105        i += node.GetLength() - 1; // skip subtrees under the node that was folded
106
107        qualityForImpactsCalculation = newQualityForImpactsCalculation;
108      }
109      return model.SymbolicExpressionTree;
110    }
111  }
112}
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