1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2018 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.Collections.Generic;
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23 | using HEAL.Attic;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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28 |
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29 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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30 | [Item("Pearson R² Constraint Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")]
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31 | [StorableType("D61462E4-2032-4790-B63D-5E6512987F64")]
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32 | public class SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
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33 | [StorableConstructor]
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34 | protected SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(StorableConstructorFlag _) : base(_) { }
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35 | protected SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator original, Cloner cloner)
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36 | : base(original, cloner) {
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37 | }
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38 | public override IDeepCloneable Clone(Cloner cloner) {
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39 | return new SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(this, cloner);
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40 | }
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41 |
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42 | public SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator() : base() { }
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43 |
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44 | public override bool Maximization { get { return true; } }
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45 |
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46 | public override IOperation InstrumentedApply() {
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47 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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48 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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49 |
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50 | double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
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51 | QualityParameter.ActualValue = new DoubleValue(quality);
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52 | return base.InstrumentedApply();
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53 | }
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54 |
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55 | public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
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56 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
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57 | IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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58 | OnlineCalculatorError errorState = OnlineCalculatorError.None;
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59 |
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60 | var constraints = problemData.IntervalConstraints.EnabledConstraints;
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61 | var variableRanges = problemData.VariableRanges.GetIntervals();
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62 | var tree = solution;
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63 |
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64 | if (!SymbolicRegressionConstraintAnalyzer.ConstraintsSatisfied(constraints, variableRanges, tree)) {
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65 | return 0;
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66 | }
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67 |
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68 | var r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit,
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69 | upperEstimationLimit, problemData, rows, applyLinearScaling);
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70 |
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71 | return r2;
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72 | }
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73 |
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74 |
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75 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree,
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76 | IRegressionProblemData problemData, IEnumerable<int> rows) {
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77 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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78 | EstimationLimitsParameter.ExecutionContext = context;
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79 | ApplyLinearScalingParameter.ExecutionContext = context;
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80 |
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81 | double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
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82 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows,
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83 | ApplyLinearScalingParameter.ActualValue.Value);
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84 |
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85 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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86 | EstimationLimitsParameter.ExecutionContext = null;
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87 | ApplyLinearScalingParameter.ExecutionContext = null;
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88 |
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89 | return r2;
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90 | }
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91 | }
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92 | }
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