1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 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 HEAL.Attic;
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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 |
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30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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31 | [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic regression solution.")]
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32 | [StorableType("B1EFB303-9C37-4CBB-8269-BDBC223D9086")]
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33 | public class SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
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34 | [StorableConstructor]
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35 | protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(StorableConstructorFlag _) : base(_) { }
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36 | protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator original, Cloner cloner)
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37 | : base(original, cloner) {
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38 | }
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39 | public override IDeepCloneable Clone(Cloner cloner) {
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40 | return new SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(this, cloner);
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41 | }
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42 |
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43 | public SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator() : base() { }
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44 |
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45 | public override IEnumerable<bool> Maximization { get { return new bool[2] { false, false }; } }
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46 |
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47 | public override IOperation InstrumentedApply() {
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48 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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49 | var tree = SymbolicExpressionTreeParameter.ActualValue;
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50 | var problemData = ProblemDataParameter.ActualValue;
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51 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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52 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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53 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
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54 |
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55 | if (UseParameterOptimization) {
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56 | SymbolicRegressionParameterOptimizationEvaluator.OptimizeParameters(interpreter, tree, problemData, rows, applyLinearScaling, ParameterOptimizationIterations, updateVariableWeights: ParameterOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
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57 | }
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58 |
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59 | double[] qualities = Calculate(
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60 | tree, ProblemDataParameter.ActualValue,
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61 | rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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62 | ApplyLinearScalingParameter.ActualValue.Value,
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63 | EstimationLimitsParameter.ActualValue.Lower,
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64 | EstimationLimitsParameter.ActualValue.Upper,
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65 | DecimalPlaces);
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66 | QualitiesParameter.ActualValue = new DoubleArray(qualities);
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67 | return base.InstrumentedApply();
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68 | }
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69 |
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70 | public static double[] Calculate(
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71 | ISymbolicExpressionTree tree,
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72 | IRegressionProblemData problemData,
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73 | IEnumerable<int> rows,
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74 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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75 | bool applyLinearScaling,
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76 | double lowerEstimationLimit, double upperEstimationLimit,
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77 | int decimalPlaces) {
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78 | var mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(
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79 | tree, problemData, rows,
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80 | interpreter, applyLinearScaling,
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81 | lowerEstimationLimit,
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82 | upperEstimationLimit);
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83 |
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84 | if (decimalPlaces >= 0)
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85 | mse = Math.Round(mse, decimalPlaces);
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86 |
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87 | return new double[2] { mse, tree.Length };
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88 | }
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89 |
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90 | public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
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91 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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92 | EstimationLimitsParameter.ExecutionContext = context;
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93 | ApplyLinearScalingParameter.ExecutionContext = context;
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94 |
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95 | double[] quality = Calculate(
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96 | tree, problemData, rows,
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97 | SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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98 | ApplyLinearScalingParameter.ActualValue.Value,
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99 | EstimationLimitsParameter.ActualValue.Lower,
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100 | EstimationLimitsParameter.ActualValue.Upper, DecimalPlaces);
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101 |
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102 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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103 | EstimationLimitsParameter.ExecutionContext = null;
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104 | ApplyLinearScalingParameter.ExecutionContext = null;
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105 |
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106 | return quality;
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107 | }
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108 | }
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109 | }
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