[5505] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[15583] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5505] | 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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[12147] | 22 | using System;
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[5505] | 23 | using System.Collections.Generic;
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[16499] | 24 | using System.Diagnostics;
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| 25 | using System.Linq;
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[5505] | 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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[16499] | 30 | using HeuristicLab.Parameters;
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[5505] | 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[16628] | 32 | using HEAL.Attic;
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[5505] | 33 |
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[5618] | 34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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[16499] | 35 | [Item("Pearson R² & Average Similarity Evaluator", "Calculates the Pearson R² and the average similarity of a symbolic regression solution candidate.")]
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[16628] | 36 | [StorableType("AC638211-90FD-448A-BC32-4F26411D8D9D")]
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[16499] | 37 | public class PearsonRSquaredAverageSimilarityEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
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| 38 | private const string StrictSimilarityParameterName = "StrictSimilarity";
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| 39 |
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| 40 | private readonly object locker = new object();
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| 41 |
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| 42 | public IFixedValueParameter<BoolValue> StrictSimilarityParameter {
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| 43 | get { return (IFixedValueParameter<BoolValue>)Parameters[StrictSimilarityParameterName]; }
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| 44 | }
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| 45 |
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| 46 | public bool StrictSimilarity {
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| 47 | get { return StrictSimilarityParameter.Value.Value; }
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| 48 | }
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| 49 |
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[5505] | 50 | [StorableConstructor]
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[16628] | 51 | protected PearsonRSquaredAverageSimilarityEvaluator(StorableConstructorFlag _) : base(_) { }
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[16499] | 52 | protected PearsonRSquaredAverageSimilarityEvaluator(PearsonRSquaredAverageSimilarityEvaluator original, Cloner cloner)
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[5505] | 53 | : base(original, cloner) {
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| 54 | }
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| 55 | public override IDeepCloneable Clone(Cloner cloner) {
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[16499] | 56 | return new PearsonRSquaredAverageSimilarityEvaluator(this, cloner);
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[5505] | 57 | }
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| 58 |
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[16499] | 59 | public PearsonRSquaredAverageSimilarityEvaluator() : base() {
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| 60 | Parameters.Add(new FixedValueParameter<BoolValue>(StrictSimilarityParameterName, "Use strict similarity calculation.", new BoolValue(false)));
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| 61 | }
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[11310] | 62 |
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[13300] | 63 | public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize model complexity
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[5514] | 64 |
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[10291] | 65 | public override IOperation InstrumentedApply() {
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[5505] | 66 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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[5851] | 67 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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[11310] | 68 | var problemData = ProblemDataParameter.ActualValue;
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| 69 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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| 70 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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| 71 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
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| 72 |
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| 73 | if (UseConstantOptimization) {
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[13670] | 74 | SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
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[11310] | 75 | }
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[12848] | 76 | double[] qualities = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling, DecimalPlaces);
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[5505] | 77 | QualitiesParameter.ActualValue = new DoubleArray(qualities);
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[10291] | 78 | return base.InstrumentedApply();
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[5505] | 79 | }
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| 80 |
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[16499] | 81 | public double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
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[11310] | 82 | double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
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[12848] | 83 | if (decimalPlaces >= 0)
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| 84 | r2 = Math.Round(r2, decimalPlaces);
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[16499] | 85 |
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| 86 | var variables = ExecutionContext.Scope.Variables;
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| 87 | if (!variables.ContainsKey("AverageSimilarity")) {
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| 88 | lock (locker) {
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| 89 | CalculateAverageSimilarities(ExecutionContext.Scope.Parent.SubScopes.Where(x => x.Variables.ContainsKey("SymbolicExpressionTree")).ToArray(), StrictSimilarity);
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| 90 |
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| 91 | }
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| 92 | }
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| 93 |
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| 94 | double avgSim = ((DoubleValue)variables["AverageSimilarity"].Value).Value;
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| 95 | return new double[2] { r2, avgSim };
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[5505] | 96 | }
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[5613] | 97 |
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| 98 | public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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[5722] | 99 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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[5770] | 100 | EstimationLimitsParameter.ExecutionContext = context;
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[8664] | 101 | ApplyLinearScalingParameter.ExecutionContext = context;
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[13300] | 102 | // DecimalPlaces parameter is a FixedValueParameter and doesn't need the context.
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[5722] | 103 |
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[12848] | 104 | double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
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[5722] | 105 |
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| 106 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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[5770] | 107 | EstimationLimitsParameter.ExecutionContext = null;
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[8664] | 108 | ApplyLinearScalingParameter.ExecutionContext = null;
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[5722] | 109 |
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| 110 | return quality;
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[5613] | 111 | }
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[16499] | 112 |
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| 113 | private readonly Stopwatch sw = new Stopwatch();
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| 114 | public void CalculateAverageSimilarities(IScope[] treeScopes, bool strict) {
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| 115 | var trees = treeScopes.Select(x => (ISymbolicExpressionTree)x.Variables["SymbolicExpressionTree"].Value).ToArray();
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| 116 | var similarityMatrix = SymbolicExpressionTreeHash.ComputeSimilarityMatrix(trees, simplify: false, strict: strict);
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| 117 |
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| 118 | for (int i = 0; i < treeScopes.Length; ++i) {
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| 119 | var scope = treeScopes[i];
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| 120 | var avgSimilarity = 0d;
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| 121 | for (int j = 0; j < trees.Length; ++j) {
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| 122 | if (i == j) continue;
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| 123 | avgSimilarity += similarityMatrix[i, j];
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| 124 | }
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| 125 | avgSimilarity /= trees.Length - 1;
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| 126 |
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| 127 | if (scope.Variables.ContainsKey("AverageSimilarity")) {
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| 128 | ((DoubleValue)scope.Variables["AverageSimilarity"].Value).Value = avgSimilarity;
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| 129 | } else {
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| 130 | scope.Variables.Add(new Core.Variable("AverageSimilarity", new DoubleValue(avgSimilarity)));
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| 131 | }
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| 132 | }
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| 133 | }
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[5505] | 134 | }
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| 135 | }
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