1 | #region License Information
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2 |
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3 | /* HeuristicLab
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4 | * Copyright (C) 2002-2014 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.Linq;
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26 | using HeuristicLab.Analysis;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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29 | using HeuristicLab.Operators;
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30 | using HeuristicLab.Optimization;
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31 | using HeuristicLab.Parameters;
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32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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33 |
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34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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35 | public class SymbolicRegressionSolutionsAnalyzer : SingleSuccessorOperator, IAnalyzer {
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36 | private const string ResultCollectionParameterName = "Results";
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37 | private const string RegressionSolutionQualitiesResultName = "Regression Solution Qualities";
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38 |
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39 | public ILookupParameter<ResultCollection> ResultCollectionParameter {
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40 | get { return (ILookupParameter<ResultCollection>)Parameters[ResultCollectionParameterName]; }
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41 | }
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42 |
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43 | public virtual bool EnabledByDefault {
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44 | get { return false; }
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45 | }
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46 |
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47 | [StorableConstructor]
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48 | protected SymbolicRegressionSolutionsAnalyzer(bool deserializing) : base(deserializing) { }
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49 | protected SymbolicRegressionSolutionsAnalyzer(SymbolicRegressionSolutionsAnalyzer original, Cloner cloner)
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50 | : base(original, cloner) { }
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51 | public override IDeepCloneable Clone(Cloner cloner) {
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52 | return new SymbolicRegressionSolutionsAnalyzer(this, cloner);
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53 | }
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54 |
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55 | public SymbolicRegressionSolutionsAnalyzer() {
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56 | Parameters.Add(new LookupParameter<ResultCollection>(ResultCollectionParameterName, "The result collection to store the analysis results."));
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57 | }
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58 |
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59 | public override IOperation Apply() {
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60 | var results = ResultCollectionParameter.ActualValue;
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61 |
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62 | if (!results.ContainsKey(RegressionSolutionQualitiesResultName)) {
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63 | var newDataTable = new DataTable(RegressionSolutionQualitiesResultName);
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64 | results.Add(new Result(RegressionSolutionQualitiesResultName, "Chart displaying the training and test qualities of the regression solutions.", newDataTable));
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65 | }
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66 |
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67 | var dataTable = (DataTable)results[RegressionSolutionQualitiesResultName].Value;
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68 | foreach (var result in results.Where(r => r.Value is IRegressionSolution)) {
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69 | var solution = (IRegressionSolution)result.Value;
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70 |
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71 | var trainingR2 = result.Name + Environment.NewLine + "Training R²";
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72 | if (!dataTable.Rows.ContainsKey(trainingR2))
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73 | dataTable.Rows.Add(new DataRow(trainingR2));
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74 |
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75 | var testR2 = result.Name + Environment.NewLine + " Test R²";
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76 | if (!dataTable.Rows.ContainsKey(testR2))
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77 | dataTable.Rows.Add(new DataRow(testR2));
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78 |
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79 | dataTable.Rows[trainingR2].Values.Add(solution.TrainingRSquared);
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80 | dataTable.Rows[testR2].Values.Add(solution.TestRSquared);
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81 | }
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82 |
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83 | return base.Apply();
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84 | }
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85 | }
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86 | }
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