Changeset 16499
 Timestamp:
 01/04/19 15:04:26 (6 months ago)
 Location:
 trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4
 Files:

 1 edited
 1 copied
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trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression3.4.csproj
r15198 r16499 121 121 <Compile Include="MultiObjective\PearsonRSquaredNestedTreeSizeEvaluator.cs" /> 122 122 <Compile Include="MultiObjective\PearsonRSquaredNumberOfVariablesEvaluator.cs" /> 123 <Compile Include="MultiObjective\PearsonRSquaredAverageSimilarityEvaluator.cs" /> 123 124 <Compile Include="MultiObjective\PearsonRSquaredTreeComplexityEvaluator.cs" /> 124 125 <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs" /> 
trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredAverageSimilarityEvaluator.cs
r16303 r16499 22 22 using System; 23 23 using System.Collections.Generic; 24 using System.Diagnostics; 25 using System.Linq; 24 26 using HeuristicLab.Common; 25 27 using HeuristicLab.Core; 26 28 using HeuristicLab.Data; 27 29 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; 30 using HeuristicLab.Parameters; 28 31 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; 29 32 30 33 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { 31 [Item("Pearson R² & Tree Complexity Evaluator", "Calculates the Pearson R² and the tree complexity of a symbolic regression solution.")]34 [Item("Pearson R² & Average Similarity Evaluator", "Calculates the Pearson R² and the average similarity of a symbolic regression solution candidate.")] 32 35 [StorableClass] 33 public class PearsonRSquaredTreeComplexityEvaluator : SymbolicRegressionMultiObjectiveEvaluator { 36 public class PearsonRSquaredAverageSimilarityEvaluator : SymbolicRegressionMultiObjectiveEvaluator { 37 private const string StrictSimilarityParameterName = "StrictSimilarity"; 38 39 private readonly object locker = new object(); 40 41 public IFixedValueParameter<BoolValue> StrictSimilarityParameter { 42 get { return (IFixedValueParameter<BoolValue>)Parameters[StrictSimilarityParameterName]; } 43 } 44 45 public bool StrictSimilarity { 46 get { return StrictSimilarityParameter.Value.Value; } 47 } 48 34 49 [StorableConstructor] 35 protected PearsonRSquared TreeComplexityEvaluator(bool deserializing) : base(deserializing) { }36 protected PearsonRSquared TreeComplexityEvaluator(PearsonRSquaredTreeComplexityEvaluator original, Cloner cloner)50 protected PearsonRSquaredAverageSimilarityEvaluator(bool deserializing) : base(deserializing) { } 51 protected PearsonRSquaredAverageSimilarityEvaluator(PearsonRSquaredAverageSimilarityEvaluator original, Cloner cloner) 37 52 : base(original, cloner) { 38 53 } 39 54 public override IDeepCloneable Clone(Cloner cloner) { 40 return new PearsonRSquared TreeComplexityEvaluator(this, cloner);55 return new PearsonRSquaredAverageSimilarityEvaluator(this, cloner); 41 56 } 42 57 43 public PearsonRSquaredTreeComplexityEvaluator() : base() { } 58 public PearsonRSquaredAverageSimilarityEvaluator() : base() { 59 Parameters.Add(new FixedValueParameter<BoolValue>(StrictSimilarityParameterName, "Use strict similarity calculation.", new BoolValue(false))); 60 } 44 61 45 62 public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize model complexity … … 61 78 } 62 79 63 public staticdouble[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {80 public double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) { 64 81 double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); 65 82 if (decimalPlaces >= 0) 66 83 r2 = Math.Round(r2, decimalPlaces); 67 return new double[2] { r2, SymbolicDataAnalysisModelComplexityCalculator.CalculateComplexity(solution) }; 84 85 var variables = ExecutionContext.Scope.Variables; 86 if (!variables.ContainsKey("AverageSimilarity")) { 87 lock (locker) { 88 CalculateAverageSimilarities(ExecutionContext.Scope.Parent.SubScopes.Where(x => x.Variables.ContainsKey("SymbolicExpressionTree")).ToArray(), StrictSimilarity); 89 90 } 91 } 92 93 double avgSim = ((DoubleValue)variables["AverageSimilarity"].Value).Value; 94 return new double[2] { r2, avgSim }; 68 95 } 69 96 … … 82 109 return quality; 83 110 } 111 112 private readonly Stopwatch sw = new Stopwatch(); 113 public void CalculateAverageSimilarities(IScope[] treeScopes, bool strict) { 114 var trees = treeScopes.Select(x => (ISymbolicExpressionTree)x.Variables["SymbolicExpressionTree"].Value).ToArray(); 115 var similarityMatrix = SymbolicExpressionTreeHash.ComputeSimilarityMatrix(trees, simplify: false, strict: strict); 116 117 for (int i = 0; i < treeScopes.Length; ++i) { 118 var scope = treeScopes[i]; 119 var avgSimilarity = 0d; 120 for (int j = 0; j < trees.Length; ++j) { 121 if (i == j) continue; 122 avgSimilarity += similarityMatrix[i, j]; 123 } 124 avgSimilarity /= trees.Length  1; 125 126 if (scope.Variables.ContainsKey("AverageSimilarity")) { 127 ((DoubleValue)scope.Variables["AverageSimilarity"].Value).Value = avgSimilarity; 128 } else { 129 scope.Variables.Add(new Core.Variable("AverageSimilarity", new DoubleValue(avgSimilarity))); 130 } 131 } 132 } 84 133 } 85 134 }
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