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.Threading;
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24 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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25 | using HeuristicLab.MainForm;
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26 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Views;
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27 |
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28 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.Views {
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29 | public partial class InteractiveSymbolicRegressionSolutionSimplifierView : InteractiveSymbolicDataAnalysisSolutionSimplifierView {
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30 | public new SymbolicRegressionSolution Content {
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31 | get { return (SymbolicRegressionSolution)base.Content; }
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32 | set { base.Content = value; }
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33 | }
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34 |
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35 | public InteractiveSymbolicRegressionSolutionSimplifierView()
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36 | : base(new SymbolicRegressionSolutionImpactValuesCalculator()) {
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37 | InitializeComponent();
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38 | this.Caption = "Interactive Regression Solution Simplifier";
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39 | }
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40 |
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41 | protected override void SetEnabledStateOfControls() {
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42 | base.SetEnabledStateOfControls();
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43 |
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44 | var tree = Content?.Model?.SymbolicExpressionTree;
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45 | btnOptimizeConstants.Enabled = tree != null && NonlinearLeastSquaresConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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46 | btnVectorOptimizeConstants.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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47 | nudLearningRate.Enabled = tree != null && TensorFlowConstantOptimizationEvaluator.CanOptimizeConstants(tree);
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48 | }
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49 |
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50 | protected override void UpdateModel(ISymbolicExpressionTree tree) {
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51 | var model = new SymbolicRegressionModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit);
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52 | model.Scale(Content.ProblemData);
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53 | Content.Model = model;
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54 | }
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55 |
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56 | protected override ISymbolicExpressionTree OptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
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57 | const int constOptIterations = 50;
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58 | const int maxRepetitions = 100;
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59 | const double minimumImprovement = 1e-10;
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60 | var regressionProblemData = Content.ProblemData;
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61 | var model = Content.Model;
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62 | progress.CanBeStopped = true;
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63 | double prevResult = 0.0, improvement = 0.0;
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64 | var result = 0.0;
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65 | int reps = 0;
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66 |
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67 | do {
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68 | prevResult = result;
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69 | tree = NonlinearLeastSquaresConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData, regressionProblemData.TrainingIndices,
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70 | applyLinearScaling: true, maxIterations: constOptIterations, updateVariableWeights: true,
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71 | cancellationToken: cancellationToken, iterationCallback: (args, func, obj) => {
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72 | double newProgressValue = progress.ProgressValue + (1.0 / (constOptIterations + 2) / maxRepetitions); // (constOptIterations + 2) iterations are reported
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73 | progress.ProgressValue = Math.Min(newProgressValue, 1.0);
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74 | });
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75 | result = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(model.Interpreter, tree,
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76 | model.LowerEstimationLimit, model.UpperEstimationLimit, regressionProblemData, regressionProblemData.TrainingIndices, applyLinearScaling: true);
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77 | reps++;
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78 | improvement = result - prevResult;
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79 | } while (improvement > minimumImprovement && reps < maxRepetitions &&
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80 | progress.ProgressState != ProgressState.StopRequested &&
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81 | progress.ProgressState != ProgressState.CancelRequested);
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82 | return tree;
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83 | }
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84 |
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85 | protected override ISymbolicExpressionTree VectorOptimizeConstants(ISymbolicExpressionTree tree, CancellationToken cancellationToken, IProgress progress) {
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86 | const int maxIterations = 1000;
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87 | var regressionProblemData = Content.ProblemData;
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88 | progress.CanBeStopped = true;
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89 |
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90 | var learningRate = Math.Pow(10, (double)nudLearningRate.Value);
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91 |
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92 | return TensorFlowConstantOptimizationEvaluator.OptimizeTree(tree, regressionProblemData,
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93 | regressionProblemData.TrainingIndices,
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94 | applyLinearScaling: false, updateVariableWeights: true, maxIterations: maxIterations, learningRate: learningRate,
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95 | cancellationToken: cancellationToken,
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96 | progress: new SynchronousProgress<double>(cost => {
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97 | var newProgress = progress.ProgressValue + (1.0 / (maxIterations + 1));
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98 | progress.ProgressValue = Math.Min(newProgress, 1.0);
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99 | progress.Message = $"MSE: {cost}";
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100 | })
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101 | );
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102 | }
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103 |
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104 | internal class SynchronousProgress<T> : IProgress<T> {
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105 | private readonly Action<T> callback;
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106 | public SynchronousProgress(Action<T> callback) {
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107 | this.callback = callback;
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108 | }
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109 | public void Report(T value) {
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110 | callback(value);
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111 | }
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112 | }
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113 | }
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114 | }
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