[12951] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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[13480] | 23 | using System.Collections.Generic;
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[12951] | 24 | using System.Linq;
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[12979] | 25 | using System.Threading.Tasks;
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[12951] | 26 | using HeuristicLab.Common;
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| 27 | using HeuristicLab.Core;
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| 28 | using HeuristicLab.Data;
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| 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 30 | using HeuristicLab.EvolutionTracking;
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[13480] | 31 | using HeuristicLab.Optimization;
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[12951] | 32 | using HeuristicLab.Parameters;
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| 33 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 34 | using HeuristicLab.Random;
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| 35 |
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[12958] | 36 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[12951] | 37 | [Item("SchemaEvaluator", "An operator that builds schemas based on the heredity relationship in the genealogy graph.")]
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| 38 | [StorableClass]
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| 39 | public class SchemaEvaluator : EvolutionTrackingOperator<ISymbolicExpressionTree> {
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[12952] | 40 | #region parameter names
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[12951] | 41 | private const string MinimumSchemaFrequencyParameterName = "MinimumSchemaFrequency";
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| 42 | private const string MinimumPhenotypicSimilarityParameterName = "MinimumPhenotypicSimilarity";
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| 43 | private const string ReplacementRatioParameterName = "ReplacementRatio";
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| 44 | private const string SchemaParameterName = "Schema";
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| 45 | private const string PopulationSizeParameterName = "PopulationSize";
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| 46 | private const string RandomParameterName = "Random";
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| 47 | private const string EvaluatorParameterName = "Evaluator";
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| 48 | private const string ProblemDataParameterName = "ProblemData";
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| 49 | private const string InterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 50 | private const string EstimationLimitsParameterName = "EstimationLimits";
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| 51 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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| 52 | private const string MutatorParameterName = "Mutator";
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[13480] | 53 | private const string CrossoverParameterName = "Crossover";
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[12951] | 54 | private const string RandomReplacementParameterName = "RandomReplacement";
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[12988] | 55 | private const string NumberOfChangedTreesParameterName = "NumberOfChangedTrees";
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[12979] | 56 | private const string ExecuteInParallelParameterName = "ExecuteInParallel";
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| 57 | private const string MaxDegreeOfParalellismParameterName = "MaxDegreeOfParallelism";
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[12988] | 58 | private const string ExclusiveMatchingParameterName = "ExclusiveMatching";
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[13480] | 59 | private const string UseAdaptiveReplacementRatioParameterName = "UseAdaptiveReplacementRatio";
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[13496] | 60 | private const string StrictSchemaMatchingParameterName = "StrictSchemaMatching";
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[12952] | 61 | #endregion
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[12951] | 62 |
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| 63 | #region parameters
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[13480] | 64 | public ILookupParameter<BoolValue> UseAdaptiveReplacementRatioParameter {
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| 65 | get { return (ILookupParameter<BoolValue>)Parameters[UseAdaptiveReplacementRatioParameterName]; }
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| 66 | }
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[12988] | 67 | public ILookupParameter<BoolValue> ExclusiveMatchingParameter {
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| 68 | get { return (ILookupParameter<BoolValue>)Parameters[ExclusiveMatchingParameterName]; }
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| 69 | }
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[12979] | 70 | public ILookupParameter<BoolValue> ExecuteInParallelParameter {
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| 71 | get { return (ILookupParameter<BoolValue>)Parameters[ExecuteInParallelParameterName]; }
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| 72 | }
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| 73 | public ILookupParameter<IntValue> MaxDegreeOfParallelismParameter {
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| 74 | get { return (ILookupParameter<IntValue>)Parameters[MaxDegreeOfParalellismParameterName]; }
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| 75 | }
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[12951] | 76 | public ILookupParameter<ISymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>> EvaluatorParameter {
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| 77 | get { return (ILookupParameter<ISymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>>)Parameters[EvaluatorParameterName]; }
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| 78 | }
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| 79 | public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
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| 80 | get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
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| 81 | }
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| 82 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter {
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| 83 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName]; }
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| 84 | }
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| 85 | public ILookupParameter<DoubleLimit> EstimationLimitsParameter {
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| 86 | get { return (ILookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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| 87 | }
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| 88 | public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
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| 89 | get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
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| 90 | }
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| 91 | public ILookupParameter<BoolValue> RandomReplacementParameter {
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| 92 | get { return (ILookupParameter<BoolValue>)Parameters[RandomReplacementParameterName]; }
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| 93 | }
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| 94 | public ILookupParameter<ISymbolicExpressionTreeManipulator> MutatorParameter {
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| 95 | get { return (ILookupParameter<ISymbolicExpressionTreeManipulator>)Parameters[MutatorParameterName]; }
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| 96 | }
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[13480] | 97 | public ILookupParameter<ISymbolicExpressionTreeCrossover> CrossoverParameter {
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| 98 | get { return (ILookupParameter<ISymbolicExpressionTreeCrossover>)Parameters[CrossoverParameterName]; }
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| 99 | }
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[12951] | 100 | public ILookupParameter<IRandom> RandomParameter {
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| 101 | get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
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| 102 | }
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| 103 | public ILookupParameter<IntValue> PopulationSizeParameter {
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| 104 | get { return (ILookupParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
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| 105 | }
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| 106 | public ILookupParameter<ISymbolicExpressionTree> SchemaParameter {
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| 107 | get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SchemaParameterName]; }
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| 108 | }
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| 109 | public ILookupParameter<PercentValue> MinimumSchemaFrequencyParameter {
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| 110 | get { return (ILookupParameter<PercentValue>)Parameters[MinimumSchemaFrequencyParameterName]; }
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| 111 | }
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| 112 | public ILookupParameter<PercentValue> ReplacementRatioParameter {
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| 113 | get { return (ILookupParameter<PercentValue>)Parameters[ReplacementRatioParameterName]; }
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| 114 | }
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| 115 | public ILookupParameter<PercentValue> MinimumPhenotypicSimilarityParameter {
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| 116 | get { return (ILookupParameter<PercentValue>)Parameters[MinimumPhenotypicSimilarityParameterName]; }
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| 117 | }
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[12988] | 118 | public LookupParameter<IntValue> NumberOfChangedTreesParameter {
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| 119 | get { return (LookupParameter<IntValue>)Parameters[NumberOfChangedTreesParameterName]; }
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[12952] | 120 | }
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[13496] | 121 | public LookupParameter<BoolValue> StrictSchemaMatchingParameter {
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| 122 | get { return (LookupParameter<BoolValue>)Parameters[StrictSchemaMatchingParameterName]; }
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| 123 | }
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[12951] | 124 | #endregion
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| 125 |
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| 126 | #region parameter properties
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[12979] | 127 | public PercentValue MinimumSchemaFrequency { get { return MinimumSchemaFrequencyParameter.ActualValue; } }
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| 128 | public PercentValue ReplacementRatio { get { return ReplacementRatioParameter.ActualValue; } }
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| 129 | public PercentValue MinimumPhenotypicSimilarity { get { return MinimumPhenotypicSimilarityParameter.ActualValue; } }
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| 130 | public BoolValue RandomReplacement { get { return RandomReplacementParameter.ActualValue; } }
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[12988] | 131 | public IntValue NumberOfChangedTrees { get { return NumberOfChangedTreesParameter.ActualValue; } }
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[12951] | 132 | #endregion
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| 133 |
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[12952] | 134 | private readonly SymbolicExpressionTreePhenotypicSimilarityCalculator calculator = new SymbolicExpressionTreePhenotypicSimilarityCalculator();
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| 135 | private readonly QueryMatch qm;
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| 136 |
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[13480] | 137 | public Func<double, double> ReplacementRule { get; set; }
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| 138 |
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[12952] | 139 | private readonly ISymbolicExpressionTreeNodeEqualityComparer comp = new SymbolicExpressionTreeNodeEqualityComparer {
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| 140 | MatchConstantValues = false,
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| 141 | MatchVariableWeights = false,
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| 142 | MatchVariableNames = true
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| 143 | };
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| 144 |
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[12988] | 145 | public ISymbolicExpressionTree Schema { get; set; }
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| 146 |
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[12979] | 147 | [Storable]
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| 148 | private readonly UpdateEstimatedValuesOperator updateEstimatedValuesOperator;
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[12952] | 149 |
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[13496] | 150 | [StorableHook(HookType.AfterDeserialization)]
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| 151 | private void AfterDeserialization() {
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| 152 | if (!Parameters.ContainsKey(StrictSchemaMatchingParameterName))
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| 153 | Parameters.Add(new LookupParameter<BoolValue>(StrictSchemaMatchingParameterName));
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| 154 | }
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| 155 |
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[12951] | 156 | public SchemaEvaluator() {
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| 157 | qm = new QueryMatch(comp) { MatchParents = true };
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[12979] | 158 | this.updateEstimatedValuesOperator = new UpdateEstimatedValuesOperator();
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[12988] | 159 | #region add parameters
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[12951] | 160 | Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SchemaParameterName, "The current schema to be evaluated"));
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| 161 | Parameters.Add(new LookupParameter<PercentValue>(MinimumSchemaFrequencyParameterName));
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| 162 | Parameters.Add(new LookupParameter<PercentValue>(ReplacementRatioParameterName));
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| 163 | Parameters.Add(new LookupParameter<PercentValue>(MinimumPhenotypicSimilarityParameterName));
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| 164 | Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(PopulationSizeParameterName));
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| 165 | Parameters.Add(new LookupParameter<IRandom>(RandomParameterName));
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| 166 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>>(EvaluatorParameterName));
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| 167 | Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName));
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| 168 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(InterpreterParameterName));
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| 169 | Parameters.Add(new LookupParameter<DoubleLimit>(EstimationLimitsParameterName));
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| 170 | Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName));
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[13496] | 171 | Parameters.Add(new LookupParameter<BoolValue>(StrictSchemaMatchingParameterName));
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[12951] | 172 | Parameters.Add(new LookupParameter<ISymbolicExpressionTreeManipulator>(MutatorParameterName));
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[13480] | 173 | Parameters.Add(new LookupParameter<ISymbolicExpressionTreeCrossover>(CrossoverParameterName));
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[12951] | 174 | Parameters.Add(new LookupParameter<BoolValue>(RandomReplacementParameterName));
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[12988] | 175 | Parameters.Add(new LookupParameter<IntValue>(NumberOfChangedTreesParameterName));
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[12979] | 176 | Parameters.Add(new LookupParameter<BoolValue>(ExecuteInParallelParameterName));
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| 177 | Parameters.Add(new LookupParameter<IntValue>(MaxDegreeOfParalellismParameterName));
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[12988] | 178 | Parameters.Add(new LookupParameter<BoolValue>(ExclusiveMatchingParameterName));
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[13480] | 179 | Parameters.Add(new LookupParameter<BoolValue>(UseAdaptiveReplacementRatioParameterName));
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[12988] | 180 | #endregion
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[12951] | 181 | }
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| 182 |
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[12966] | 183 | [StorableConstructor]
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| 184 | protected SchemaEvaluator(bool deserializing) : base(deserializing) { }
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| 185 |
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[12951] | 186 | protected SchemaEvaluator(SchemaEvaluator original, Cloner cloner) : base(original, cloner) {
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[12979] | 187 | this.comp = original.comp == null ? new SymbolicExpressionTreeNodeEqualityComparer {
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| 188 | MatchConstantValues = false,
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| 189 | MatchVariableWeights = false,
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| 190 | MatchVariableNames = true
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| 191 | } : (ISymbolicExpressionTreeNodeEqualityComparer)original.comp.Clone();
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| 192 | this.qm = new QueryMatch(comp) { MatchParents = original.qm?.MatchParents ?? true };
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| 193 | this.updateEstimatedValuesOperator = new UpdateEstimatedValuesOperator();
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[12951] | 194 | }
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| 195 |
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| 196 | public override IDeepCloneable Clone(Cloner cloner) {
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| 197 | return new SchemaEvaluator(this, cloner);
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| 198 | }
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| 199 |
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| 200 | public override IOperation Apply() {
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[13496] | 201 | var strictSchemaMatching = StrictSchemaMatchingParameter.ActualValue.Value;
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| 202 | if (strictSchemaMatching) {
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| 203 | comp.MatchVariableWeights = true;
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| 204 | comp.MatchConstantValues = true;
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| 205 | } else {
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| 206 | comp.MatchVariableWeights = false;
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| 207 | comp.MatchConstantValues = false;
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| 208 | }
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| 209 |
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[12951] | 210 | var individuals = ExecutionContext.Scope.SubScopes; // the scopes represent the individuals
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[13480] | 211 | var trees = individuals.Select(x => (ISymbolicExpressionTree)x.Variables["SymbolicExpressionTree"].Value).ToList();
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[12951] | 212 |
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| 213 | var random = RandomParameter.ActualValue;
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| 214 | var mutator = MutatorParameter.ActualValue;
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| 215 |
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[12988] | 216 | var s = Schema;
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[12979] | 217 | var sRoot = s.Root.GetSubtree(0).GetSubtree(0);
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| 218 | int countThreshold = (int)Math.Max(2, Math.Round(MinimumSchemaFrequency.Value * individuals.Count));
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| 219 |
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| 220 | // first apply the length and root equality checks in order to filter the individuals
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[12988] | 221 | var exclusiveMatching = ExclusiveMatchingParameter.ActualValue.Value;
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[13480] | 222 | var filtered = new List<int>();
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| 223 | for (int i = 0; i < individuals.Count; ++i) {
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| 224 | if (exclusiveMatching && individuals[i].Variables.ContainsKey("AlreadyMatched")) continue;
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| 225 | var t = trees[i];
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| 226 | var tRoot = t.Root.GetSubtree(0).GetSubtree(0);
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| 227 | if (t.Length < s.Length || !qm.Comparer.Equals(tRoot, sRoot)) continue;
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| 228 | filtered.Add(i);
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| 229 | }
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[12979] | 230 |
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| 231 | // if we don't have enough filtered individuals, then we are done
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[13480] | 232 | // if the schema exceeds the minimum frequency, it gets processed further
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[12979] | 233 | if (filtered.Count < countThreshold) {
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| 234 | return base.Apply();
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| 235 | }
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| 236 |
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| 237 | // check if the filtered individuals match the schema
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| 238 | var sNodes = QueryMatch.InitializePostOrder(sRoot);
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[12958] | 239 | var matchingIndividuals = new ScopeList();
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[12979] | 240 | bool executeInParallel = ExecuteInParallelParameter.ActualValue.Value;
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| 241 | int maxDegreeOfParallelism = MaxDegreeOfParallelismParameter.ActualValue.Value;
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| 242 |
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| 243 | if (executeInParallel) {
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| 244 | var matching = new bool[filtered.Count];
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| 245 |
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| 246 | Parallel.For(0, filtered.Count, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism },
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| 247 | i => {
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[13480] | 248 | var index = filtered[i];
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| 249 | var t = trees[index];
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[12979] | 250 | var tNodes = QueryMatch.InitializePostOrder(t.Root.GetSubtree(0).GetSubtree(0));
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| 251 | if (qm.Match(tNodes, sNodes)) {
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| 252 | matching[i] = true;
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| 253 | }
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| 254 | });
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| 255 |
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[13480] | 256 | matchingIndividuals.AddRange(filtered.Where((x, i) => matching[i]).Select(x => individuals[x]));
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[12979] | 257 | } else {
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| 258 | for (int i = 0; i < filtered.Count; ++i) {
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| 259 | // break early if it becomes impossible to reach the minimum threshold
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| 260 | if (matchingIndividuals.Count + filtered.Count - i < countThreshold)
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| 261 | break;
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| 262 |
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[13480] | 263 | var index = filtered[i];
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| 264 | var tRoot = trees[index].Root.GetSubtree(0).GetSubtree(0);
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| 265 | var tNodes = QueryMatch.InitializePostOrder(tRoot);
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[12979] | 266 | if (qm.Match(tNodes, sNodes))
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[13480] | 267 | matchingIndividuals.Add(individuals[index]);
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[12979] | 268 | }
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[12958] | 269 | }
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[12951] | 270 |
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[12979] | 271 | if (matchingIndividuals.Count < countThreshold) {
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[12951] | 272 | return base.Apply();
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[12952] | 273 | }
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[12951] | 274 |
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[13480] | 275 | var similarity = CalculateSimilarity(matchingIndividuals, calculator, executeInParallel, maxDegreeOfParallelism);
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[12952] | 276 | if (similarity < MinimumPhenotypicSimilarity.Value) {
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[12951] | 277 | return base.Apply();
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[12952] | 278 | }
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[12951] | 279 |
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[13480] | 280 | double replacementRatio;
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| 281 | var adaptiveReplacementRatio = UseAdaptiveReplacementRatioParameter.ActualValue.Value;
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| 282 |
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| 283 | if (adaptiveReplacementRatio) {
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| 284 | var r = (double)matchingIndividuals.Count / individuals.Count;
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| 285 | replacementRatio = ReplacementRule(r);
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| 286 | } else {
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| 287 | replacementRatio = ReplacementRatio.Value;
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| 288 | }
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| 289 |
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| 290 | int n = (int)Math.Floor(matchingIndividuals.Count * replacementRatio);
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[12952] | 291 | var individualsToReplace = RandomReplacement.Value ? matchingIndividuals.SampleRandomWithoutRepetition(random, n).ToList()
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| 292 | : matchingIndividuals.OrderBy(x => (DoubleValue)x.Variables["Quality"].Value).Take(n).ToList();
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[12979] | 293 | var mutationOc = new OperationCollection { Parallel = false }; // cannot be parallel due to the before/after operators which insert vertices in the genealogy graph
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| 294 | var updateEstimatedValues = new OperationCollection { Parallel = true }; // evaluation should be done in parallel when possible
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[12951] | 295 | foreach (var ind in individualsToReplace) {
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| 296 | var mutatorOp = ExecutionContext.CreateChildOperation(mutator, ind);
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[12979] | 297 | var updateOp = ExecutionContext.CreateChildOperation(updateEstimatedValuesOperator, ind);
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| 298 | mutationOc.Add(mutatorOp);
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| 299 | updateEstimatedValues.Add(updateOp);
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[12988] | 300 | if (exclusiveMatching)
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| 301 | ind.Variables.Add(new Core.Variable("AlreadyMatched"));
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[12951] | 302 | }
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[12988] | 303 |
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| 304 | NumberOfChangedTrees.Value += individualsToReplace.Count; // a lock is not necessary here because the SchemaEvaluators cannot be executed in parallel
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| 305 |
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[12979] | 306 | return new OperationCollection(mutationOc, updateEstimatedValues, base.Apply());
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[12951] | 307 | }
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[12979] | 308 |
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[13480] | 309 | public static double CalculateSimilarity(ScopeList individuals, ISolutionSimilarityCalculator calculator, bool parallel = false, int nThreads = -1) {
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[12979] | 310 | double similarity = 0;
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| 311 | int count = individuals.Count;
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[13480] | 312 | if (count < 2) return double.NaN;
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[12979] | 313 | if (parallel) {
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| 314 | var parallelOptions = new ParallelOptions { MaxDegreeOfParallelism = nThreads };
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| 315 | var simArray = new double[count - 1];
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| 316 | Parallel.For(0, count - 1, parallelOptions, i => {
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| 317 | double innerSim = 0;
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| 318 | for (int j = i + 1; j < count; ++j) {
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| 319 | innerSim += calculator.CalculateSolutionSimilarity(individuals[i], individuals[j]);
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| 320 | }
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| 321 | simArray[i] = innerSim;
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| 322 | });
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| 323 | similarity = simArray.Sum();
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| 324 | } else {
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| 325 | for (int i = 0; i < count - 1; ++i) {
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| 326 | for (int j = i + 1; j < count; ++j) {
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| 327 | similarity += calculator.CalculateSolutionSimilarity(individuals[i], individuals[j]);
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| 328 | }
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| 329 | }
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| 330 | }
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| 331 | return similarity / (count * (count - 1) / 2.0);
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| 332 | }
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[12951] | 333 | }
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| 334 | }
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