#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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic regression solution.")] [StorableType("B1EFB303-9C37-4CBB-8269-BDBC223D9086")] public class SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator { [StorableConstructor] protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(StorableConstructorFlag _) : base(_) { } protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(this, cloner); } public SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator() : base() { } public override IEnumerable Maximization { get { return new bool[2] { false, false }; } } public override IOperation InstrumentedApply() { IEnumerable rows = GenerateRowsToEvaluate(); var tree = SymbolicExpressionTreeParameter.ActualValue; var problemData = ProblemDataParameter.ActualValue; var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; var estimationLimits = EstimationLimitsParameter.ActualValue; var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value; if (UseConstantOptimization) { SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper); } double[] qualities = Calculate( tree, ProblemDataParameter.ActualValue, rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ApplyLinearScalingParameter.ActualValue.Value, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, DecimalPlaces); QualitiesParameter.ActualValue = new DoubleArray(qualities); return base.InstrumentedApply(); } public static double[] Calculate( ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, bool applyLinearScaling, double lowerEstimationLimit, double upperEstimationLimit, int decimalPlaces) { var mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate( tree, problemData, rows, interpreter, applyLinearScaling, lowerEstimationLimit, upperEstimationLimit); if (decimalPlaces >= 0) mse = Math.Round(mse, decimalPlaces); return new double[2] { mse, tree.Length }; } public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; double[] quality = Calculate( tree, problemData, rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ApplyLinearScalingParameter.ActualValue.Value, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, DecimalPlaces); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return quality; } } }