1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 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 |
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23 | using System;
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24 | using System.Collections.Generic;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Data;
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29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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30 | using HeuristicLab.Parameters;
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31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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32 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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34 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
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35 |
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36 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Evaluators {
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37 | [Item("SymbolicRegressionConditionalPearsonsRSquaredEvaluator", "Evaluates a symbolic regression solution.")]
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38 | [StorableClass]
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39 | public class SymbolicRegressionConditionalPearsonsRSquaredEvaluator : SingleObjectiveSymbolicRegressionEvaluator {
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40 | private const string ConditionVariableParameterName = "ConditionVariable";
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41 |
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42 | public IValueParameter<StringValue> ConditionVariableParameter {
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43 | get { return (IValueParameter<StringValue>)Parameters[ConditionVariableParameterName]; }
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44 | }
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45 | public StringValue ConditionVariable {
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46 | get { return ConditionVariableParameter.Value; }
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47 | }
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48 |
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49 | public SymbolicRegressionConditionalPearsonsRSquaredEvaluator()
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50 | : base() {
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51 | Parameters.Add(new ValueLookupParameter<StringValue>(ConditionVariableParameterName, "The variable name that states which samples should be skipped for evaluation."));
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52 | }
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53 | [StorableConstructor]
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54 | protected SymbolicRegressionConditionalPearsonsRSquaredEvaluator(bool deserializing) : base(deserializing) { }
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55 | protected SymbolicRegressionConditionalPearsonsRSquaredEvaluator(SymbolicRegressionConditionalPearsonsRSquaredEvaluator original, Cloner cloner)
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56 | : base(original, cloner) {
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57 | }
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58 | public override IDeepCloneable Clone(Cloner cloner) {
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59 | return new SymbolicRegressionConditionalPearsonsRSquaredEvaluator(this, cloner);
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60 | }
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61 |
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62 | public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
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63 | double mse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows, ConditionVariable.Value);
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64 | return mse;
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65 | }
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66 |
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67 | public static double Calculate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows, string conditionVariable) {
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68 | IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, dataset, rows);
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69 | IEnumerable<double> originalValues = dataset.GetEnumeratedVariableValues(targetVariable, rows);
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70 | IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
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71 | IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
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72 | IEnumerator<int> rowsEnumerator = rows.GetEnumerator();
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73 | OnlinePearsonsRSquaredEvaluator r2Evaluator = new OnlinePearsonsRSquaredEvaluator();
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74 |
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75 | int minLag = 0;
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76 | var laggedTreeNodes = solution.IterateNodesPrefix().OfType<LaggedVariableTreeNode>();
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77 | if (laggedTreeNodes.Any())
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78 | minLag = laggedTreeNodes.Min(laggedTreeNode => laggedTreeNode.Symbol.MinLag);
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79 |
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80 | while (originalEnumerator.MoveNext() && estimatedEnumerator.MoveNext() && rowsEnumerator.MoveNext()) {
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81 | double estimated = estimatedEnumerator.Current;
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82 | double original = originalEnumerator.Current;
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83 | int row = rowsEnumerator.Current;
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84 |
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85 | bool evaluate = true;
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86 | for (int i = minLag; i <= 0 && evaluate; i++) {
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87 | evaluate = evaluate && dataset[conditionVariable, row - i].IsAlmost(0.0);
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88 | }
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89 |
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90 | if (evaluate)
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91 | if (dataset[conditionVariable, row].IsAlmost(0.0) && dataset[conditionVariable, row - 1].IsAlmost(0.0)) {
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92 | if (double.IsNaN(estimated))
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93 | estimated = upperEstimationLimit;
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94 | else
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95 | estimated = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, estimated));
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96 | r2Evaluator.Add(original, estimated);
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97 | }
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98 | }
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99 |
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100 | if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext() || rowsEnumerator.MoveNext()) {
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101 | throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
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102 | } else {
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103 | return r2Evaluator.RSquared;
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104 | }
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105 | }
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106 |
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107 |
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108 | }
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109 | }
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