#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 System.Diagnostics; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Parameters; using HEAL.Attic; 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 readonly object locker = new object(); public IFixedValueParameter StrictSimilarityParameter { get { return (IFixedValueParameter)Parameters[StrictSimilarityParameterName]; } } 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))); } public override IEnumerable Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize model complexity 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[] qualities = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling, DecimalPlaces); QualitiesParameter.ActualValue = new DoubleArray(qualities); return base.InstrumentedApply(); } public double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable rows, bool applyLinearScaling, int decimalPlaces) { double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (decimalPlaces >= 0) r2 = Math.Round(r2, decimalPlaces); var variables = ExecutionContext.Scope.Variables; if (!variables.ContainsKey("AverageSimilarity")) { lock (locker) { CalculateAverageSimilarities(ExecutionContext.Scope.Parent.SubScopes.Where(x => x.Variables.ContainsKey("SymbolicExpressionTree")).ToArray(), StrictSimilarity); } } double avgSim = ((DoubleValue)variables["AverageSimilarity"].Value).Value; return new double[2] { r2, avgSim }; } public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; // DecimalPlaces parameter is a FixedValueParameter and doesn't need the context. double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return quality; } private readonly Stopwatch sw = new Stopwatch(); public void CalculateAverageSimilarities(IScope[] treeScopes, bool strict) { var trees = treeScopes.Select(x => (ISymbolicExpressionTree)x.Variables["SymbolicExpressionTree"].Value).ToArray(); var similarityMatrix = SymbolicExpressionTreeHash.ComputeSimilarityMatrix(trees, simplify: false, strict: strict); for (int i = 0; i < treeScopes.Length; ++i) { var scope = treeScopes[i]; var avgSimilarity = 0d; for (int j = 0; j < trees.Length; ++j) { if (i == j) continue; avgSimilarity += similarityMatrix[i, j]; } avgSimilarity /= trees.Length - 1; if (scope.Variables.ContainsKey("AverageSimilarity")) { ((DoubleValue)scope.Variables["AverageSimilarity"].Value).Value = avgSimilarity; } else { scope.Variables.Add(new Core.Variable("AverageSimilarity", new DoubleValue(avgSimilarity))); } } } } }