Index: branches/HLScript/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj
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--- branches/HLScript/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj (revision 10331)
+++ branches/HLScript/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj (revision 10358)
@@ -120,4 +120,5 @@
+
Index: branches/HLScript/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs
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--- branches/HLScript/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs (revision 10358)
+++ branches/HLScript/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs (revision 10358)
@@ -0,0 +1,88 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2013 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 HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
+ [Item("Mean relative error Evaluator", "Evaluator for symbolic regression models that calculates the mean relative error avg(|y'-y| / |y|+1)." +
+ "The +1 is necessary to handle data with the value of 0.0 correctly. " +
+ "Notice: Linear scaling is ignored for this evaluator.")]
+ [StorableClass]
+ public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
+ public override bool Maximization { get { return false; } }
+ [StorableConstructor]
+ protected SymbolicRegressionMeanRelativeErrorEvaluator(bool deserializing) : base(deserializing) { }
+ protected SymbolicRegressionMeanRelativeErrorEvaluator(SymbolicRegressionMeanRelativeErrorEvaluator original, Cloner cloner)
+ : base(original, cloner) {
+ }
+ public override IDeepCloneable Clone(Cloner cloner) {
+ return new SymbolicRegressionMeanRelativeErrorEvaluator(this, cloner);
+ }
+ public SymbolicRegressionMeanRelativeErrorEvaluator() : base() { }
+
+ public override IOperation InstrumentedApply() {
+ var solution = SymbolicExpressionTreeParameter.ActualValue;
+ IEnumerable rows = GenerateRowsToEvaluate();
+
+ double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
+ QualityParameter.ActualValue = new DoubleValue(quality);
+
+ return base.InstrumentedApply();
+ }
+
+ public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable rows, bool applyLinearScaling) {
+ IEnumerable estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
+ IEnumerable targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+ IEnumerable boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
+
+ var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
+
+ OnlineCalculatorError errorState;
+ OnlineCalculatorError varErrorState;
+ double mre;
+ double variance;
+ OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
+ if (errorState != OnlineCalculatorError.None) return double.NaN;
+ return mre;
+ }
+
+ public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) {
+ SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
+ EstimationLimitsParameter.ExecutionContext = context;
+ ApplyLinearScalingParameter.ExecutionContext = context;
+
+ double mre = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value);
+
+ SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
+ EstimationLimitsParameter.ExecutionContext = null;
+ ApplyLinearScalingParameter.ExecutionContext = null;
+
+ return mre;
+ }
+ }
+}