1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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32 | [Item("Mean relative error Evaluator", "Evaluator for symbolic regression models that calculates the mean relative error avg( |y' - y| / (|y| + 1))." +
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33 | "The +1 is necessary to handle data with the value of 0.0 correctly. " +
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34 | "Notice: Linear scaling is ignored for this evaluator.")]
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35 | [StorableClass]
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36 | public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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37 | public override bool Maximization { get { return false; } }
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38 | [StorableConstructor]
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39 | protected SymbolicRegressionMeanRelativeErrorEvaluator(bool deserializing) : base(deserializing) { }
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40 | protected SymbolicRegressionMeanRelativeErrorEvaluator(SymbolicRegressionMeanRelativeErrorEvaluator original, Cloner cloner)
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41 | : base(original, cloner) {
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42 | }
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43 | public override IDeepCloneable Clone(Cloner cloner) {
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44 | return new SymbolicRegressionMeanRelativeErrorEvaluator(this, cloner);
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45 | }
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46 | public SymbolicRegressionMeanRelativeErrorEvaluator() : base() { }
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47 |
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48 | public override IOperation InstrumentedApply() {
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49 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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50 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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51 |
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52 | var problemData = ProblemDataParameter.ActualValue;
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53 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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54 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows).ToArray();
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55 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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56 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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57 |
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58 | if (SaveEstimatedValuesToScope) {
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59 | var boundedValues = estimatedValues.LimitToRange(estimationLimits.Lower, estimationLimits.Upper).ToArray();
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60 | var scope = ExecutionContext.Scope;
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61 | if (scope.Variables.ContainsKey("EstimatedValues"))
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62 | scope.Variables["EstimatedValues"].Value = new DoubleArray(boundedValues);
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63 | else
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64 | scope.Variables.Add(new Core.Variable("EstimatedValues", new DoubleArray(boundedValues)));
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65 | }
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66 |
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67 | double quality = Calculate(targetValues, estimatedValues, estimationLimits.Lower, estimationLimits.Upper);
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68 | QualityParameter.ActualValue = new DoubleValue(quality);
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69 |
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70 | return base.InstrumentedApply();
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71 | }
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72 |
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73 | public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
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74 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
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75 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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76 | return Calculate(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit);
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77 | }
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78 |
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79 | private static double Calculate(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues, double lowerEstimationLimit, double upperEstimationLimit) {
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80 | IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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81 |
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82 | var relResiduals = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Abs(t - e) / (Math.Abs(t) + 1.0));
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83 |
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84 | OnlineCalculatorError errorState;
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85 | OnlineCalculatorError varErrorState;
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86 | double mre;
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87 | double variance;
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88 | OnlineMeanAndVarianceCalculator.Calculate(relResiduals, out mre, out variance, out errorState, out varErrorState);
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89 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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90 | return mre;
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91 | }
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92 |
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93 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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94 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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95 | EstimationLimitsParameter.ExecutionContext = context;
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96 |
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97 | double mre = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
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98 |
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99 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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100 | EstimationLimitsParameter.ExecutionContext = null;
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101 |
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102 | return mre;
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103 | }
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104 | }
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105 | } |
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