1 | #region License Information
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2 |
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3 | /* HeuristicLab
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4 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 |
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22 | #endregion
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23 |
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24 | using System;
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25 | using System.Collections.Generic;
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26 | using HEAL.Attic;
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27 | using HeuristicLab.Analysis;
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28 | using HeuristicLab.Common;
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29 | using HeuristicLab.Core;
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30 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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31 | using HeuristicLab.Optimization;
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32 | using HeuristicLab.Parameters;
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33 |
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34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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35 | [StorableType("4318C6BD-E0A1-45FE-AC30-96E7F73B51FB")]
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36 | public class SymbolicRegressionConstraintAnalyzer : SymbolicDataAnalysisAnalyzer, ISymbolicDataAnalysisInterpreterOperator, ISymbolicExpressionTreeAnalyzer {
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37 | private const string ConstraintViolationsResultName = "Constraint Violations";
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38 |
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39 | private const string ProblemDataParameterName = "ProblemData";
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40 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
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41 |
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42 | #region parameter properties
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43 | public ILookupParameter<RegressionProblemData> RegressionProblemDataParameter {
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44 | get { return (ILookupParameter<RegressionProblemData>)Parameters[ProblemDataParameterName]; }
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45 | }
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46 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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47 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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48 | }
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49 | #endregion
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50 | #region properties
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51 |
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52 | public RegressionProblemData RegressionProblemData {
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53 | get { return RegressionProblemDataParameter.ActualValue; }
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54 | }
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55 |
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56 | public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter {
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57 | get { return SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; }
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58 | }
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59 | #endregion
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60 |
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61 | public virtual bool EnabledByDefault {
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62 | get { return false; }
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63 | }
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64 |
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65 | [StorableConstructor]
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66 | protected SymbolicRegressionConstraintAnalyzer(StorableConstructorFlag _) : base(_) {
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67 | }
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68 |
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69 | protected SymbolicRegressionConstraintAnalyzer(SymbolicRegressionConstraintAnalyzer original, Cloner cloner)
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70 | : base(original, cloner) {
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71 | }
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72 |
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73 | public override IDeepCloneable Clone(Cloner cloner) {
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74 | return new SymbolicRegressionConstraintAnalyzer(this, cloner);
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75 | }
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76 |
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77 | public SymbolicRegressionConstraintAnalyzer() {
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78 | Parameters.Add(new LookupParameter<RegressionProblemData>(ProblemDataParameterName, "The problem data of the symbolic data analysis problem."));
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79 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter for symbolic data analysis expression trees."));
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80 | }
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81 |
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82 | [StorableHook(HookType.AfterDeserialization)]
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83 | private void AfterDeserialization() {
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84 | }
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85 |
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86 | public override IOperation Apply() {
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87 | var results = ResultCollectionParameter.ActualValue;
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88 | var intervalInterpreter = new IntervalInterpreter();
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89 | if (!results.ContainsKey(ConstraintViolationsResultName)) {
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90 | var newDataTable = new DataTable(ConstraintViolationsResultName);
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91 | results.Add(new Result(ConstraintViolationsResultName, "Chart displaying the constraint violations.",
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92 | newDataTable));
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93 | }
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94 |
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95 | var dataTable = (DataTable)results[ConstraintViolationsResultName].Value;
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96 | var problemData = RegressionProblemData;
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97 |
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98 | var constraintViolations = new Dictionary<string, int>();
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99 |
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100 | var constraints = IntervalConstraintsParser.Parse(problemData.IntervalConstraints.Value);
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101 | var variableRanges = problemData.VariableRanges.VariableIntervals;
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102 |
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103 | if (dataTable.Rows.Count == 0) {
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104 | foreach (var constraint in constraints) {
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105 | if (!dataTable.Rows.ContainsKey(constraint.Expression)) {
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106 | dataTable.Rows.Add(new DataRow(constraint.Expression));
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107 | }
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108 | }
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109 | }
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110 |
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111 | foreach (var constraint in constraints) {
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112 | constraintViolations.Add(constraint.Expression, 0);
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113 | }
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114 |
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115 | foreach (var tree in this.SymbolicExpressionTree) {
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116 | foreach (var constraint in constraints) {
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117 | if (constraint.Variable != null && !variableRanges.ContainsKey(constraint.Variable))
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118 | throw new ArgumentException(
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119 | $"The given variable {constraint.Variable} in the constraint does not exists in the model.",
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120 | nameof(IntervalConstraintsParser));
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121 | if (!constraint.IsDerivation) {
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122 | var res = intervalInterpreter.GetSymbolicExpressionTreeInterval(tree,
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123 | variableRanges);
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124 | if (!constraint.Interval.Contains(res, constraint.InclusiveLowerBound,
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125 | constraint.InclusiveUpperBound)) {
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126 | constraintViolations[constraint.Expression]++;
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127 | }
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128 | } else {
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129 | var dTree = tree;
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130 | for (var i = 0; i < constraint.NumberOfDerivation; ++i) {
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131 | dTree = DerivativeCalculator.Derive(dTree, constraint.Variable);
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132 | }
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133 |
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134 | var res = intervalInterpreter.GetSymbolicExpressionTreeInterval(dTree,
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135 | variableRanges);
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136 | if (!constraint.Interval.Contains(res, constraint.InclusiveLowerBound,
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137 | constraint.InclusiveUpperBound)) {
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138 | constraintViolations[constraint.Expression]++;
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139 | }
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140 | }
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141 | }
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142 | }
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143 |
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144 | foreach (var kvp in constraintViolations) {
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145 | dataTable.Rows[kvp.Key].Values.Add(kvp.Value);
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146 | }
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147 | return base.Apply();
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148 | }
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149 | }
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150 | }
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