#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("Log Residual Evaluator", "Evaluator for symbolic regression models that calculates the mean of logarithmic absolute residuals avg(log( 1 + abs(y' - y)))" + "This evaluator does not perform linear scaling!" + "This evaluator can be useful if the modeled function contains discontinuities (e.g. 1/x). " + "For some data sets (e.g. Korns benchmark instances containing inverses of near zero values) the squared error or absolute " + "error put too much emphasis on modeling the outlier values. Using log-residuals instead has the " + "effect that smaller residuals have a stronger impact on the total quality compared to the large residuals." + "This effects GP convergence because functional fragments which are necessary to explain small variations are also more likely" + " to stay in the population. This is useful even when the actual objective function is mean of squared errors.")] [StorableType("8CEA1A56-167D-481B-9167-C1DED8E06680")] public class SymbolicRegressionLogResidualEvaluator : SymbolicRegressionSingleObjectiveEvaluator { [StorableConstructor] protected SymbolicRegressionLogResidualEvaluator(StorableConstructorFlag _) : base(_) { } protected SymbolicRegressionLogResidualEvaluator(SymbolicRegressionLogResidualEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionLogResidualEvaluator(this, cloner); } public SymbolicRegressionLogResidualEvaluator() : base() { } public override bool Maximization { get { return false; } } public override IOperation InstrumentedApply() { var tree = SymbolicExpressionTreeParameter.ActualValue; IEnumerable rows = GenerateRowsToEvaluate(); double quality = Calculate( tree, ProblemDataParameter.ActualValue, rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); QualityParameter.ActualValue = new DoubleValue(quality); return base.InstrumentedApply(); } public static double Calculate( ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit, double upperEstimationLimit) { IEnumerable estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows); IEnumerable targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); IEnumerable boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); var logRes = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Log(1.0 + Math.Abs(e - t))); OnlineCalculatorError errorState; OnlineCalculatorError varErrorState; double mlr; double variance; OnlineMeanAndVarianceCalculator.Calculate(logRes, out mlr, out variance, out errorState, out varErrorState); if (errorState != OnlineCalculatorError.None) return double.NaN; return mlr; } public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; double mlr = Calculate( tree, problemData, rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; return mlr; } public override double Evaluate( ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, bool applyLinearScaling = true, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) { return Calculate(tree, problemData, rows, interpreter, lowerEstimationLimit, upperEstimationLimit); } } }