#region License Information /* HeuristicLab * Copyright (C) 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.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [Item("Pearson R² & Nested Tree size Evaluator", "Calculates the Pearson R² and the nested tree size of a symbolic regression solution.")] [StorableType("37486F27-9BA0-4C89-BB2F-6823E4FB317A")] public class PearsonRSquaredNestedTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator { [StorableConstructor] protected PearsonRSquaredNestedTreeSizeEvaluator(StorableConstructorFlag _) : base(_) { } protected PearsonRSquaredNestedTreeSizeEvaluator(PearsonRSquaredNestedTreeSizeEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new PearsonRSquaredNestedTreeSizeEvaluator(this, cloner); } public PearsonRSquaredNestedTreeSizeEvaluator() : base() { } public override IEnumerable Maximization { get { return new bool[2] { true, false }; } } // maximize R² & minimize nested tree size 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(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces); QualitiesParameter.ActualValue = new DoubleArray(qualities); return base.InstrumentedApply(); } public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable rows, bool applyLinearScaling, int decimalPlaces) { double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate( tree, problemData, rows, interpreter, applyLinearScaling, lowerEstimationLimit, upperEstimationLimit); if (decimalPlaces >= 0) r2 = Math.Round(r2, decimalPlaces); return new double[2] { r2, tree.IterateNodesPostfix().Sum(n => n.GetLength()) }; // sum of the length of the whole sub-tree for each node } 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; } } }