1 | using System;
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2 | using System.Collections.Generic;
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3 | using System.Linq;
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4 | using System.Text;
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5 | using System.Threading.Tasks;
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6 | using HeuristicLab.Core;
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7 | using HeuristicLab.Optimization;
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8 | using HEAL.Attic;
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9 | using HeuristicLab.Common;
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10 | using HeuristicLab.Problems.Instances;
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11 | using HeuristicLab.Parameters;
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12 | using HeuristicLab.Data;
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13 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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14 | using HeuristicLab.PluginInfrastructure;
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15 | using HeuristicLab.Problems.Instances.DataAnalysis;
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16 |
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17 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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18 | [StorableType("7464E84B-65CC-440A-91F0-9FA920D730F9")]
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19 | [Item(Name = "Structured Symbolic Regression Single Objective Problem (single-objective)", Description = "A problem with a structural definition and unfixed subfunctions.")]
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20 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 150)]
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21 | public class StructuredSymbolicRegressionSingleObjectiveProblem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData> {
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22 |
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23 | #region Constants
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24 | private const string TreeEvaluatorParameterName = "TreeEvaluator";
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25 | private const string ProblemDataParameterName = "ProblemData";
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26 | private const string StructureTemplateParameterName = "Structure Template";
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27 | private const string InterpreterParameterName = "Interpreter";
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28 | private const string EstimationLimitsParameterName = "EstimationLimits";
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29 | private const string BestTrainingSolutionParameterName = "Best Training Solution";
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30 |
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31 | private const string SymbolicExpressionTreeName = "SymbolicExpressionTree";
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32 | private const string VariableName = "Variable";
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33 |
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34 | private const string StructureTemplateDescriptionText =
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35 | "Enter your expression as string in infix format into the empty input field.\n" +
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36 | "By checking the \"Apply Linear Scaling\" checkbox you can add the relevant scaling terms to your expression.\n" +
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37 | "After entering the expression click parse to build the tree.\n" +
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38 | "To edit the defined sub-functions, click on the coressponding colored node in the tree view.";
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39 | #endregion
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40 |
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41 | #region Parameters
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42 | public IConstrainedValueParameter<SymbolicRegressionSingleObjectiveEvaluator> TreeEvaluatorParameter => (IConstrainedValueParameter<SymbolicRegressionSingleObjectiveEvaluator>)Parameters[TreeEvaluatorParameterName];
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43 | public IValueParameter<IRegressionProblemData> ProblemDataParameter => (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName];
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44 | public IFixedValueParameter<StructureTemplate> StructureTemplateParameter => (IFixedValueParameter<StructureTemplate>)Parameters[StructureTemplateParameterName];
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45 | public IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter => (IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName];
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46 | public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter => (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName];
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47 | public IResultParameter<ISymbolicRegressionSolution> BestTrainingSolutionParameter => (IResultParameter<ISymbolicRegressionSolution>)Parameters[BestTrainingSolutionParameterName];
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48 | #endregion
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49 |
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50 | #region Properties
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51 |
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52 | public IRegressionProblemData ProblemData {
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53 | get => ProblemDataParameter.Value;
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54 | set {
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55 | ProblemDataParameter.Value = value;
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56 | ProblemDataChanged?.Invoke(this, EventArgs.Empty);
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57 | }
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58 | }
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59 |
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60 | public StructureTemplate StructureTemplate => StructureTemplateParameter.Value;
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61 |
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62 | public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter => InterpreterParameter.Value;
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63 |
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64 | IParameter IDataAnalysisProblem.ProblemDataParameter => ProblemDataParameter;
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65 | IDataAnalysisProblemData IDataAnalysisProblem.ProblemData => ProblemData;
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66 |
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67 | public DoubleLimit EstimationLimits => EstimationLimitsParameter.Value;
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68 |
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69 | public override bool Maximization => false;
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70 | #endregion
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71 |
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72 | #region EventHandlers
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73 | public event EventHandler ProblemDataChanged;
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74 | #endregion
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75 |
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76 | #region Constructors & Cloning
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77 | public StructuredSymbolicRegressionSingleObjectiveProblem() {
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78 | var provider = new PhysicsInstanceProvider();
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79 | var descriptor = new SheetBendingProcess();
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80 | var problemData = provider.LoadData(descriptor);
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81 | var shapeConstraintProblemData = new ShapeConstrainedRegressionProblemData(problemData);
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82 |
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83 |
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84 | var targetInterval = shapeConstraintProblemData.VariableRanges.GetInterval(shapeConstraintProblemData.TargetVariable);
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85 | var estimationWidth = targetInterval.Width * 10;
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86 |
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87 |
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88 | var structureTemplate = new StructureTemplate();
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89 | structureTemplate.Changed += OnTemplateChanged;
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90 |
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91 | var evaluators = new ItemSet<SymbolicRegressionSingleObjectiveEvaluator>(
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92 | ApplicationManager.Manager.GetInstances<SymbolicRegressionSingleObjectiveEvaluator>()
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93 | .Where(x => x.Maximization == Maximization));
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94 |
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95 | Parameters.Add(new ConstrainedValueParameter<SymbolicRegressionSingleObjectiveEvaluator>(
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96 | TreeEvaluatorParameterName,
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97 | evaluators,
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98 | evaluators.First()));
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99 |
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100 | Parameters.Add(new ValueParameter<IRegressionProblemData>(
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101 | ProblemDataParameterName,
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102 | shapeConstraintProblemData));
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103 | ProblemDataParameter.ValueChanged += ProblemDataParameterValueChanged;
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104 |
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105 | Parameters.Add(new FixedValueParameter<StructureTemplate>(
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106 | StructureTemplateParameterName,
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107 | StructureTemplateDescriptionText,
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108 | structureTemplate));
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109 |
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110 | Parameters.Add(new ValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(
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111 | InterpreterParameterName,
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112 | new SymbolicDataAnalysisExpressionTreeInterpreter()) { Hidden = true });
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113 |
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114 | Parameters.Add(new FixedValueParameter<DoubleLimit>(
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115 | EstimationLimitsParameterName,
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116 | new DoubleLimit(targetInterval.LowerBound - estimationWidth, targetInterval.UpperBound + estimationWidth)) { Hidden = true });
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117 |
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118 | Parameters.Add(new ResultParameter<ISymbolicRegressionSolution>(BestTrainingSolutionParameterName, "") { Hidden = true });
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119 |
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120 | this.EvaluatorParameter.Hidden = true;
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121 |
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122 | Operators.Add(new SymbolicDataAnalysisVariableFrequencyAnalyzer());
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123 | Operators.Add(new MinAverageMaxSymbolicExpressionTreeLengthAnalyzer());
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124 | Operators.Add(new SymbolicExpressionSymbolFrequencyAnalyzer());
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125 |
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126 | StructureTemplate.Template =
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127 | "(" +
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128 | "(210000 / (210000 + h)) * ((sigma_y * t * t) / (wR * Rt * t)) + " +
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129 | "PlasticHardening(_) - Elasticity(_)" +
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130 | ")" +
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131 | " * C(_)";
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132 | }
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133 |
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134 | public StructuredSymbolicRegressionSingleObjectiveProblem(StructuredSymbolicRegressionSingleObjectiveProblem original,
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135 | Cloner cloner) : base(original, cloner) { }
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136 |
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137 | [StorableConstructor]
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138 | protected StructuredSymbolicRegressionSingleObjectiveProblem(StorableConstructorFlag _) : base(_) { }
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139 | #endregion
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140 |
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141 | #region Cloning
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142 | public override IDeepCloneable Clone(Cloner cloner) =>
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143 | new StructuredSymbolicRegressionSingleObjectiveProblem(this, cloner);
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144 | #endregion
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145 |
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146 | private void ProblemDataParameterValueChanged(object sender, EventArgs e) {
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147 | StructureTemplate.Reset();
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148 | // InfoBox for Reset?
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149 | }
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150 |
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151 | private void OnTemplateChanged(object sender, EventArgs args) {
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152 | SetupStructureTemplate();
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153 | }
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154 |
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155 | private void SetupStructureTemplate() {
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156 | foreach (var e in Encoding.Encodings.ToArray())
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157 | Encoding.Remove(e);
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158 |
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159 | foreach (var f in StructureTemplate.SubFunctions.Values) {
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160 | SetupVariables(f);
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161 | if (!Encoding.Encodings.Any(x => x.Name == f.Name)) // to prevent the same encoding twice
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162 | Encoding.Add(new SymbolicExpressionTreeEncoding(
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163 | f.Name,
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164 | f.Grammar,
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165 | f.MaximumSymbolicExpressionTreeLength,
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166 | f.MaximumSymbolicExpressionTreeDepth));
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167 | }
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168 | }
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169 |
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170 | public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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171 | base.Analyze(individuals, qualities, results, random);
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172 |
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173 | var best = GetBestIndividual(individuals, qualities).Item1;
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174 |
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175 | if (!results.ContainsKey(BestTrainingSolutionParameter.ActualName)) {
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176 | results.Add(new Result(BestTrainingSolutionParameter.ActualName, typeof(SymbolicRegressionSolution)));
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177 | }
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178 |
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179 | var tree = (ISymbolicExpressionTree)best[SymbolicExpressionTreeName];
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180 |
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181 | var model = new SymbolicRegressionModel(ProblemData.TargetVariable, tree, Interpreter);
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182 | var solution = model.CreateRegressionSolution(ProblemData);
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183 |
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184 | results[BestTrainingSolutionParameter.ActualName].Value = solution;
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185 | }
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186 |
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187 |
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188 | public override double Evaluate(Individual individual, IRandom random) {
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189 | var tree = BuildTree(individual);
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190 |
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191 | if (StructureTemplate.ApplyLinearScaling)
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192 | AdjustLinearScalingParams(ProblemData, tree, Interpreter);
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193 |
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194 | individual[SymbolicExpressionTreeName] = tree;
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195 |
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196 | // dpiringe: needed when Maximization = true
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197 | if (TreeEvaluatorParameter.Value is SymbolicRegressionConstantOptimizationEvaluator constantOptEvaluator) {
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198 | constantOptEvaluator.RandomParameter.Value = random;
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199 | constantOptEvaluator.RelativeNumberOfEvaluatedSamplesParameter.Value =
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200 | (PercentValue)constantOptEvaluator.ConstantOptimizationRowsPercentage.Clone();
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201 | }
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202 |
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203 | return TreeEvaluatorParameter.Value.Evaluate(
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204 | tree, ProblemData,
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205 | ProblemData.TrainingIndices,
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206 | Interpreter,
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207 | StructureTemplate.ApplyLinearScaling,
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208 | EstimationLimits.Lower,
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209 | EstimationLimits.Upper);
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210 | }
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211 |
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212 | private static void AdjustLinearScalingParams(IRegressionProblemData problemData, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter) {
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213 | var offsetNode = tree.Root.GetSubtree(0).GetSubtree(0);
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214 | var scalingNode = offsetNode.Subtrees.Where(x => !(x is ConstantTreeNode)).First();
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215 |
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216 | var offsetConstantNode = (ConstantTreeNode)offsetNode.Subtrees.Where(x => x is ConstantTreeNode).First();
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217 | var scalingConstantNode = (ConstantTreeNode)scalingNode.Subtrees.Where(x => x is ConstantTreeNode).First();
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218 |
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219 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TrainingIndices);
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220 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices);
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221 |
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222 | OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out double a, out double b, out OnlineCalculatorError error);
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223 | if (error == OnlineCalculatorError.None) {
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224 | offsetConstantNode.Value = a;
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225 | scalingConstantNode.Value = b;
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226 | }
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227 | }
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228 |
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229 | private ISymbolicExpressionTree BuildTree(Individual individual) {
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230 | if (StructureTemplate.Tree == null)
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231 | throw new ArgumentException("No structure template defined!");
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232 |
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233 | var templateTree = (ISymbolicExpressionTree)StructureTemplate.Tree.Clone();
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234 |
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235 | // build main tree
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236 | foreach (var subFunctionTreeNode in templateTree.IterateNodesPrefix().OfType<SubFunctionTreeNode>()) {
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237 | var subFunctionTree = individual.SymbolicExpressionTree(subFunctionTreeNode.Name);
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238 |
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239 | // add new tree
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240 | var subTree = subFunctionTree.Root.GetSubtree(0) // Start
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241 | .GetSubtree(0); // Offset
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242 | subTree = (ISymbolicExpressionTreeNode)subTree.Clone();
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243 | subFunctionTreeNode.AddSubtree(subTree);
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244 |
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245 | }
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246 | return templateTree;
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247 | }
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248 |
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249 | private void SetupVariables(SubFunction subFunction) {
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250 | var varSym = (Variable)subFunction.Grammar.GetSymbol(VariableName);
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251 | if (varSym == null) {
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252 | varSym = new Variable();
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253 | subFunction.Grammar.AddSymbol(varSym);
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254 | }
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255 |
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256 | var allVariables = ProblemData.InputVariables.Select(x => x.Value);
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257 | var allInputs = allVariables.Where(x => x != ProblemData.TargetVariable);
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258 |
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259 | // set all variables
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260 | varSym.AllVariableNames = allVariables;
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261 |
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262 | // set all allowed variables
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263 | if (subFunction.Arguments.Contains("_")) {
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264 | varSym.VariableNames = allInputs;
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265 | } else {
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266 | var vars = new List<string>();
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267 | var exceptions = new List<Exception>();
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268 | foreach (var arg in subFunction.Arguments) {
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269 | if (allInputs.Contains(arg))
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270 | vars.Add(arg);
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271 | else
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272 | exceptions.Add(new ArgumentException($"The argument '{arg}' for sub-function '{subFunction.Name}' is not a valid variable."));
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273 | }
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274 | if (exceptions.Any())
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275 | throw new AggregateException(exceptions);
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276 | varSym.VariableNames = vars;
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277 | }
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278 |
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279 | varSym.Enabled = true;
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280 | }
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281 |
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282 | public void Load(IRegressionProblemData data) {
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283 | ProblemData = data;
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284 | StructureTemplate.Template = "f(_)";
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285 | }
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286 | }
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287 | }
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