1 | #region License Information
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2 |
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3 | /* HeuristicLab
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4 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 |
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22 | #endregion
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23 |
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24 | using System.Collections.Generic;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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29 | using HeuristicLab.Parameters;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 |
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32 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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33 | [StorableClass]
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34 | [Item("SymbolicClassificationPruningOperator", "An operator which prunes symbolic classificaton trees.")]
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35 | public class SymbolicClassificationPruningOperator : SymbolicDataAnalysisExpressionPruningOperator {
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36 | private const string ModelCreatorParameterName = "ModelCreator";
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37 | private const string EvaluatorParameterName = "Evaluator";
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38 |
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39 | #region parameter properties
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40 | public ILookupParameter<ISymbolicClassificationModelCreator> ModelCreatorParameter {
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41 | get { return (ILookupParameter<ISymbolicClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
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42 | }
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43 |
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44 | public ILookupParameter<ISymbolicClassificationSingleObjectiveEvaluator> EvaluatorParameter {
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45 | get {
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46 | return (ILookupParameter<ISymbolicClassificationSingleObjectiveEvaluator>)Parameters[EvaluatorParameterName];
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47 | }
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48 | }
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49 | #endregion
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50 |
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51 | protected SymbolicClassificationPruningOperator(SymbolicClassificationPruningOperator original, Cloner cloner) : base(original, cloner) { }
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52 | public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationPruningOperator(this, cloner); }
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53 |
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54 | [StorableConstructor]
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55 | protected SymbolicClassificationPruningOperator(bool deserializing) : base(deserializing) { }
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56 |
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57 | public SymbolicClassificationPruningOperator(ISymbolicDataAnalysisSolutionImpactValuesCalculator impactValuesCalculator)
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58 | : base(impactValuesCalculator) {
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59 | Parameters.Add(new LookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName));
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60 | Parameters.Add(new LookupParameter<ISymbolicClassificationSingleObjectiveEvaluator>(EvaluatorParameterName));
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61 | }
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62 |
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63 | [StorableHook(HookType.AfterDeserialization)]
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64 | private void AfterDeserialization() {
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65 | // BackwardsCompatibility3.3
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66 | #region Backwards compatible code, remove with 3.4
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67 | base.ImpactValuesCalculator = new SymbolicClassificationSolutionImpactValuesCalculator();
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68 | if (!Parameters.ContainsKey(EvaluatorParameterName)) {
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69 | Parameters.Add(new LookupParameter<ISymbolicClassificationSingleObjectiveEvaluator>(EvaluatorParameterName));
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70 | }
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71 | #endregion
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72 | }
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73 |
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74 | protected override ISymbolicDataAnalysisModel CreateModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
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75 | var classificationProblemData = (IClassificationProblemData)problemData;
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76 | var model = ModelCreatorParameter.ActualValue.CreateSymbolicClassificationModel(classificationProblemData.TargetVariable, tree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
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77 |
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78 | var rows = classificationProblemData.TrainingIndices;
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79 | model.RecalculateModelParameters(classificationProblemData, rows);
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80 | return model;
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81 | }
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82 |
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83 | protected override double Evaluate(IDataAnalysisModel model) {
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84 | var evaluator = EvaluatorParameter.ActualValue;
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85 | var classificationModel = (ISymbolicClassificationModel)model;
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86 | var classificationProblemData = (IClassificationProblemData)ProblemDataParameter.ActualValue;
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87 | var rows = Enumerable.Range(FitnessCalculationPartitionParameter.ActualValue.Start, FitnessCalculationPartitionParameter.ActualValue.Size);
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88 | return evaluator.Evaluate(this.ExecutionContext, classificationModel.SymbolicExpressionTree, classificationProblemData, rows);
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89 | }
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90 |
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91 | public static ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, ISymbolicClassificationModelCreator modelCreator,
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92 | SymbolicClassificationSolutionImpactValuesCalculator impactValuesCalculator, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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93 | IClassificationProblemData problemData, DoubleLimit estimationLimits, IEnumerable<int> rows,
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94 | double nodeImpactThreshold = 0.0, bool pruneOnlyZeroImpactNodes = false) {
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95 | var clonedTree = (ISymbolicExpressionTree)tree.Clone();
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96 | var model = modelCreator.CreateSymbolicClassificationModel(problemData.TargetVariable, clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
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97 |
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98 | var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
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99 | double qualityForImpactsCalculation = double.NaN;
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100 |
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101 | for (int i = 0; i < nodes.Count; ++i) {
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102 | var node = nodes[i];
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103 | if (node is ConstantTreeNode) continue;
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104 |
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105 | double impactValue, replacementValue, newQualityForImpactsCalculation;
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106 | impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, out newQualityForImpactsCalculation, qualityForImpactsCalculation);
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107 |
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108 | if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
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109 | if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold) continue;
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110 |
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111 | var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
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112 | constantNode.Value = replacementValue;
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113 |
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114 | ReplaceWithConstant(node, constantNode);
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115 | i += node.GetLength() - 1; // skip subtrees under the node that was folded
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116 |
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117 | qualityForImpactsCalculation = newQualityForImpactsCalculation;
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118 | }
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119 | return model.SymbolicExpressionTree;
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120 | }
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121 | }
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122 | }
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