#region License Information /* HeuristicLab * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using HEAL.Attic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Parameters; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [Item("Pearson R² & Average Similarity Evaluator", "Calculates the Pearson R² and the average similarity of a symbolic regression solution candidate.")] [StorableType("FE514989-E619-48B8-AC8E-9A2202708F65")] public class PearsonRSquaredAverageSimilarityEvaluator : SymbolicRegressionMultiObjectiveEvaluator { private const string StrictSimilarityParameterName = "StrictSimilarity"; private const string AverageSimilarityParameterName = "AverageSimilarity"; private readonly object locker = new object(); private readonly SymbolicDataAnalysisExpressionTreeAverageSimilarityCalculator SimilarityCalculator = new SymbolicDataAnalysisExpressionTreeAverageSimilarityCalculator(); public IFixedValueParameter StrictSimilarityParameter { get { return (IFixedValueParameter)Parameters[StrictSimilarityParameterName]; } } public ILookupParameter AverageSimilarityParameter { get { return (ILookupParameter)Parameters[AverageSimilarityParameterName]; } } public bool StrictSimilarity { get { return StrictSimilarityParameter.Value.Value; } } [StorableConstructor] protected PearsonRSquaredAverageSimilarityEvaluator(StorableConstructorFlag _) : base(_) { } protected PearsonRSquaredAverageSimilarityEvaluator(PearsonRSquaredAverageSimilarityEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new PearsonRSquaredAverageSimilarityEvaluator(this, cloner); } public PearsonRSquaredAverageSimilarityEvaluator() : base() { Parameters.Add(new FixedValueParameter(StrictSimilarityParameterName, "Use strict similarity calculation.", new BoolValue(false))); Parameters.Add(new LookupParameter(AverageSimilarityParameterName)); } public override IEnumerable Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize average similarity public override IOperation InstrumentedApply() { IEnumerable rows = GenerateRowsToEvaluate(); var solution = SymbolicExpressionTreeParameter.ActualValue; var problemData = ProblemDataParameter.ActualValue; var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; var estimationLimits = EstimationLimitsParameter.ActualValue; var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value; if (UseConstantOptimization) { SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper); } double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling); if (DecimalPlaces >= 0) r2 = Math.Round(r2, DecimalPlaces); lock (locker) { if (AverageSimilarityParameter.ActualValue == null) { var context = new ExecutionContext(null, SimilarityCalculator, ExecutionContext.Scope.Parent); SimilarityCalculator.StrictSimilarity = StrictSimilarity; SimilarityCalculator.Execute(context, CancellationToken); } } var avgSimilarity = AverageSimilarityParameter.ActualValue.Value; QualitiesParameter.ActualValue = new DoubleArray(new[] { r2, avgSimilarity }); return base.InstrumentedApply(); } public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; AverageSimilarityParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; var estimationLimits = EstimationLimitsParameter.ActualValue; var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value; double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling); lock (locker) { if (AverageSimilarityParameter.ActualValue == null) { var ctx = new ExecutionContext(null, SimilarityCalculator, context.Scope.Parent); SimilarityCalculator.StrictSimilarity = StrictSimilarity; SimilarityCalculator.Execute(context, CancellationToken); } } var avgSimilarity = AverageSimilarityParameter.ActualValue.Value; SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return new[] { r2, avgSimilarity }; } } }