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 |
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15 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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16 | [StorableType("7464E84B-65CC-440A-91F0-9FA920D730F9")]
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17 | [Item(Name = "Structured Symbolic Regression Single Objective Problem (single-objective)", Description = "A problem with a structural definition and unfixed subfunctions.")]
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18 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 150)]
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19 | public class StructuredSymbolicRegressionSingleObjectiveProblem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<RegressionProblemData> {
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20 |
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21 | #region Constants
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22 | private const string ProblemDataParameterName = "ProblemData";
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23 | private const string StructureDefinitionParameterName = "Structure Definition";
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24 | private const string StructureTemplateParameterName = "Structure Template";
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25 | #endregion
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26 |
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27 | #region Parameter
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28 | public IValueParameter<IRegressionProblemData> ProblemDataParameter => (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName];
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29 | public IFixedValueParameter<StringValue> StructureDefinitionParameter => (IFixedValueParameter<StringValue>)Parameters[StructureDefinitionParameterName];
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30 | public IFixedValueParameter<StructureTemplate> StructureTemplateParameter => (IFixedValueParameter<StructureTemplate>)Parameters[StructureTemplateParameterName];
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31 | #endregion
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32 |
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33 | #region Properties
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34 | public IRegressionProblemData ProblemData {
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35 | get => ProblemDataParameter.Value;
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36 | set {
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37 | ProblemDataParameter.Value = value;
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38 | ProblemDataChanged?.Invoke(this, EventArgs.Empty);
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39 | }
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40 | }
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41 |
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42 | public string StructureDefinition {
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43 | get => StructureDefinitionParameter.Value.Value;
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44 | set => StructureDefinitionParameter.Value.Value = value;
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45 | }
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46 |
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47 | public StructureTemplate StructureTemplate {
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48 | get => StructureTemplateParameter.Value;
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49 | }
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50 |
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51 | public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter { get; } = new SymbolicDataAnalysisExpressionTreeInterpreter();
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52 |
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53 | IParameter IDataAnalysisProblem.ProblemDataParameter => ProblemDataParameter;
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54 | IDataAnalysisProblemData IDataAnalysisProblem.ProblemData => ProblemData;
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55 |
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56 | public override bool Maximization => true;
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57 | #endregion
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58 |
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59 | #region EventHandlers
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60 | public event EventHandler ProblemDataChanged;
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61 | #endregion
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62 |
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63 | #region Constructors & Cloning
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64 | public StructuredSymbolicRegressionSingleObjectiveProblem() {
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65 | var problemData = new ShapeConstrainedRegressionProblemData();
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66 |
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67 | var structureTemplate = new StructureTemplate();
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68 | structureTemplate.Changed += OnTemplateChanged;
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69 |
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70 | Parameters.Add(new ValueParameter<IRegressionProblemData>(ProblemDataParameterName, problemData));
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71 | Parameters.Add(new FixedValueParameter<StructureTemplate>(StructureTemplateParameterName, structureTemplate));
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72 |
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73 | }
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74 |
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75 | public StructuredSymbolicRegressionSingleObjectiveProblem(StructuredSymbolicRegressionSingleObjectiveProblem original, Cloner cloner) { }
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76 |
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77 | [StorableConstructor]
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78 | protected StructuredSymbolicRegressionSingleObjectiveProblem(StorableConstructorFlag _) : base(_) { }
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79 | #endregion
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80 |
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81 | #region Cloning
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82 | public override IDeepCloneable Clone(Cloner cloner) =>
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83 | new StructuredSymbolicRegressionSingleObjectiveProblem(this, cloner);
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84 | #endregion
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85 |
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86 | private void OnTemplateChanged(object sender, EventArgs args) {
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87 | SetupStructureTemplate();
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88 | }
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89 |
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90 | private void SetupStructureTemplate() {
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91 | foreach (var e in Encoding.Encodings.ToArray())
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92 | Encoding.Remove(e);
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93 |
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94 | foreach (var f in StructureTemplate.SubFunctions.Values) {
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95 | SetupVariables(f);
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96 | if(!Encoding.Encodings.Any(x => x.Name == f.Name)) // to prevent the same encoding twice
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97 | Encoding.Add(new SymbolicExpressionTreeEncoding(f.Name, f.Grammar, f.MaximumSymbolicExpressionTreeLength, f.MaximumSymbolicExpressionTreeDepth));
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98 | }
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99 | }
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100 |
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101 | public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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102 | base.Analyze(individuals, qualities, results, random);
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103 |
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104 | int bestIdx = 0;
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105 | double bestQuality = Maximization ? double.MinValue : double.MaxValue;
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106 | for(int idx = 0; idx < qualities.Length; ++idx) {
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107 | if((Maximization && qualities[idx] > bestQuality) ||
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108 | (!Maximization && qualities[idx] < bestQuality)) {
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109 | bestQuality = qualities[idx];
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110 | bestIdx = idx;
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111 | }
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112 | }
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113 |
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114 | if (results.TryGetValue("Best Tree", out IResult result)) {
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115 | var tree = BuildTree(individuals[bestIdx]);
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116 | AdjustLinearScalingParams(tree, Interpreter);
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117 | result.Value = tree;
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118 | }
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119 | else {
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120 | var tree = BuildTree(individuals[bestIdx]);
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121 | AdjustLinearScalingParams(tree, Interpreter);
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122 | results.Add(new Result("Best Tree", tree));
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123 | }
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124 |
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125 | }
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126 |
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127 | public override double Evaluate(Individual individual, IRandom random) {
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128 | var tree = BuildTree(individual);
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129 |
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130 | AdjustLinearScalingParams(tree, Interpreter);
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131 | var estimationInterval = ProblemData.VariableRanges.GetInterval(ProblemData.TargetVariable);
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132 | var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(
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133 | Interpreter, tree,
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134 | estimationInterval.LowerBound, estimationInterval.UpperBound,
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135 | ProblemData, ProblemData.TrainingIndices, false);
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136 |
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137 | return quality;
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138 | }
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139 |
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140 | private void AdjustLinearScalingParams(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter) {
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141 | var offsetNode = tree.Root.GetSubtree(0).GetSubtree(0);
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142 | var scalingNode = offsetNode.Subtrees.Where(x => !(x is ConstantTreeNode)).First();
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143 |
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144 | var offsetConstantNode = (ConstantTreeNode)offsetNode.Subtrees.Where(x => x is ConstantTreeNode).First();
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145 | var scalingConstantNode = (ConstantTreeNode)scalingNode.Subtrees.Where(x => x is ConstantTreeNode).First();
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146 |
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147 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, ProblemData.Dataset, ProblemData.TrainingIndices);
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148 | var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices);
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149 |
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150 | OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out double a, out double b, out OnlineCalculatorError error);
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151 | if(error == OnlineCalculatorError.None) {
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152 | offsetConstantNode.Value = a;
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153 | scalingConstantNode.Value = b;
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154 | }
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155 | }
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156 |
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157 | private ISymbolicExpressionTree BuildTree(Individual individual) {
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158 | var templateTree = (ISymbolicExpressionTree)StructureTemplate.Tree.Clone();
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159 |
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160 | // build main tree
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161 | foreach (var n in templateTree.IterateNodesPrefix()) {
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162 | if (n.Symbol is SubFunctionSymbol) {
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163 | var subFunctionTreeNode = n as SubFunctionTreeNode;
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164 | var subFunctionTree = individual.SymbolicExpressionTree(subFunctionTreeNode.Name);
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165 | //var parent = n.Parent;
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166 |
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167 | // remove SubFunctionTreeNode
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168 | //parent.RemoveSubtree(parent.IndexOfSubtree(subFunctionTreeNode));
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169 |
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170 | // add new tree
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171 | var subTree = subFunctionTree.Root.GetSubtree(0) // Start
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172 | .GetSubtree(0); // Offset
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173 | //parent.AddSubtree(subTree);
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174 | subFunctionTreeNode.AddSubtree(subTree);
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175 | }
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176 | }
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177 | return templateTree;
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178 | }
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179 |
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180 | private void SetupVariables(SubFunction subFunction) {
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181 | var varSym = (Variable)subFunction.Grammar.GetSymbol("Variable");
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182 | if (varSym == null) {
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183 | varSym = new Variable();
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184 | subFunction.Grammar.AddSymbol(varSym);
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185 | }
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186 |
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187 | var allVariables = ProblemData.InputVariables.Select(x => x.Value);
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188 | var allInputs = allVariables.Where(x => x != ProblemData.TargetVariable);
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189 |
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190 | // set all variables
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191 | varSym.AllVariableNames = allVariables;
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192 |
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193 | // set all allowed variables
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194 | if (subFunction.Arguments.Contains("_")) {
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195 | varSym.VariableNames = allInputs;
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196 | } else {
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197 | var vars = new List<string>();
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198 | var exceptions = new List<Exception>();
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199 | foreach (var arg in subFunction.Arguments) {
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200 | if (allInputs.Contains(arg))
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201 | vars.Add(arg);
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202 | else
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203 | exceptions.Add(new ArgumentException($"The argument '{arg}' for sub-function '{subFunction.Name}' is not a valid variable."));
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204 | }
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205 | if (exceptions.Any())
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206 | throw new AggregateException(exceptions);
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207 | varSym.VariableNames = vars;
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208 | }
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209 |
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210 | varSym.Enabled = true;
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211 | }
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212 |
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213 | public void Load(RegressionProblemData data) {
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214 | ProblemData = data;
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215 | SetupStructureTemplate();
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216 | }
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217 | }
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218 | }
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