1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Algorithms.DataAnalysis;
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26 | using HeuristicLab.MainForm;
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27 | using HeuristicLab.Problems.DataAnalysis.Views.Classification;
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28 |
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29 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.Views {
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30 | [View("Solution Comparison")]
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31 | [Content(typeof(ISymbolicClassificationSolution))]
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32 | public partial class SolutionComparisonView : ClassificationSolutionComparisonView {
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33 |
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34 | public SolutionComparisonView() {
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35 | InitializeComponent();
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36 | }
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37 |
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38 | public new ISymbolicClassificationSolution Content {
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39 | get { return (ISymbolicClassificationSolution)base.Content; }
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40 | set { base.Content = value; }
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41 | }
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42 |
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43 | protected override IEnumerable<IClassificationSolution> GenerateClassificationSolutions() {
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44 | var solutionsBase = base.GenerateClassificationSolutions();
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45 | var solutions = new List<IClassificationSolution>();
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46 |
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47 | var symbolicSolution = Content;
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48 |
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49 | // does not support lagged variables
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50 | if (symbolicSolution.Model.SymbolicExpressionTree.IterateNodesPrefix().OfType<LaggedVariableTreeNode>().Any()) return solutionsBase;
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51 |
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52 | var problemData = (IClassificationProblemData)symbolicSolution.ProblemData.Clone();
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53 | if (!problemData.TrainingIndices.Any()) return null; // don't create an comparison models if the problem does not have a training set (e.g. loaded into an existing model)
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54 |
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55 | var usedDoubleVariables =
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56 | symbolicSolution.Model.SymbolicExpressionTree.IterateNodesPostfix()
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57 | .OfType<VariableTreeNode>()
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58 | .Select(node => node.VariableName)
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59 | .Concat(
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60 | symbolicSolution.Model.SymbolicExpressionTree.IterateNodesPostfix()
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61 | .OfType<VariableConditionTreeNode>()
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62 | .Select(node => node.VariableName)
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63 | )
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64 | .Distinct();
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65 |
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66 | var usedFactorVariables =
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67 | symbolicSolution.Model.SymbolicExpressionTree.IterateNodesPostfix()
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68 | .OfType<FactorVariableTreeNode>()
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69 | .Select(node => Tuple.Create(node.VariableName, node.VariableValue))
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70 | .Distinct();
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71 |
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72 | // create a new problem and dataset
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73 | var variableNames =
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74 | usedDoubleVariables
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75 | .Concat(usedFactorVariables.Select(t => t.Item1 + "=" + t.Item2))
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76 | .Concat(new string[] { problemData.TargetVariable })
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77 | .ToArray();
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78 | var variableValues =
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79 | usedDoubleVariables.Select(name => problemData.Dataset.GetDoubleValues(name).ToList())
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80 | .Concat(
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81 | // create binary variable
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82 | usedFactorVariables.Select(t => problemData.Dataset.GetReadOnlyStringValues(t.Item1).Select(val => val == t.Item2 ? 1.0 : 0.0).ToList())
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83 | )
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84 | .Concat(new[] { problemData.Dataset.GetDoubleValues(problemData.TargetVariable).ToList() });
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85 |
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86 | var newDs = new Dataset(variableNames, variableValues);
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87 | var newProblemData = new ClassificationProblemData(newDs, variableNames.Take(variableNames.Length - 1), variableNames.Last());
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88 | newProblemData.TrainingPartition.Start = problemData.TrainingPartition.Start;
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89 | newProblemData.TrainingPartition.End = problemData.TrainingPartition.End;
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90 | newProblemData.TestPartition.Start = problemData.TestPartition.Start;
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91 | newProblemData.TestPartition.End = problemData.TestPartition.End;
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92 |
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93 | try {
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94 | var oneR = OneR.CreateOneRSolution(newProblemData);
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95 | oneR.Name = "OneR Classification Solution (subset)";
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96 | solutions.Add(oneR);
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97 | } catch (NotSupportedException) { } catch (ArgumentException) { }
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98 | try {
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99 | var lda = LinearDiscriminantAnalysis.CreateLinearDiscriminantAnalysisSolution(newProblemData);
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100 | lda.Name = "Linear Discriminant Analysis Solution (subset)";
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101 | solutions.Add(lda);
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102 | } catch (NotSupportedException) { } catch (ArgumentException) { }
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103 |
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104 | return solutionsBase.Concat(solutions);
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105 | }
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106 | }
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107 | }
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