1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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28 |
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29 | namespace HeuristicLab.Problems.DataAnalysis {
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30 | /// <summary>
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31 | /// Represents discriminant function classification data analysis models.
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32 | /// </summary>
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33 | [StorableClass]
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34 | [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")]
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35 | public abstract class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
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36 | [Storable]
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37 | private IRegressionModel model;
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38 |
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39 | [Storable]
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40 | private double[] classValues;
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41 | public IEnumerable<double> ClassValues {
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42 | get { return (double[])classValues.Clone(); }
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43 | private set { classValues = value.ToArray(); }
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44 | }
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45 |
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46 | [Storable]
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47 | private double[] thresholds;
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48 | public IEnumerable<double> Thresholds {
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49 | get { return (IEnumerable<double>)thresholds.Clone(); }
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50 | private set { thresholds = value.ToArray(); }
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51 | }
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52 |
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53 | private IDiscriminantFunctionThresholdCalculator thresholdCalculator;
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54 | [Storable]
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55 | public IDiscriminantFunctionThresholdCalculator ThresholdCalculator {
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56 | get { return thresholdCalculator; }
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57 | private set { thresholdCalculator = value; }
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58 | }
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59 |
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60 |
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61 | [StorableConstructor]
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62 | protected DiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { }
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63 | protected DiscriminantFunctionClassificationModel(DiscriminantFunctionClassificationModel original, Cloner cloner)
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64 | : base(original, cloner) {
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65 | model = cloner.Clone(original.model);
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66 | classValues = (double[])original.classValues.Clone();
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67 | thresholds = (double[])original.thresholds.Clone();
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68 | }
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69 |
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70 | public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator)
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71 | : base() {
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72 | this.name = ItemName;
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73 | this.description = ItemDescription;
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74 | this.model = model;
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75 | this.classValues = new double[0];
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76 | this.thresholds = new double[0];
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77 | this.thresholdCalculator = thresholdCalculator;
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78 | }
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79 |
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80 | [StorableHook(HookType.AfterDeserialization)]
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81 | private void AfterDeserialization() {
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82 | if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
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83 | }
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84 |
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85 | public void SetThresholdsAndClassValues(IEnumerable<double> thresholds, IEnumerable<double> classValues) {
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86 | var classValuesArr = classValues.ToArray();
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87 | var thresholdsArr = thresholds.ToArray();
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88 | if (thresholdsArr.Length != classValuesArr.Length) throw new ArgumentException();
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89 |
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90 | this.classValues = classValuesArr;
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91 | this.thresholds = thresholdsArr;
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92 | OnThresholdsChanged(EventArgs.Empty);
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93 | }
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94 |
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95 | public virtual void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable<int> rows) {
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96 | double[] classValues;
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97 | double[] thresholds;
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98 | var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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99 | var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, rows);
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100 | thresholdCalculator.Calculate(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds);
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101 | SetThresholdsAndClassValues(thresholds, classValues);
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102 | }
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103 |
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104 |
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105 | public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
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106 | return model.GetEstimatedValues(dataset, rows);
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107 | }
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108 |
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109 | public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
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110 | if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current classification model.");
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111 | foreach (var x in GetEstimatedValues(dataset, rows)) {
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112 | int classIndex = 0;
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113 | // find first threshold value which is larger than x => class index = threshold index + 1
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114 | for (int i = 0; i < thresholds.Length; i++) {
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115 | if (x > thresholds[i]) classIndex++;
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116 | else break;
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117 | }
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118 | yield return classValues.ElementAt(classIndex - 1);
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119 | }
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120 | }
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121 | #region events
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122 | public event EventHandler ThresholdsChanged;
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123 | protected virtual void OnThresholdsChanged(EventArgs e) {
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124 | var listener = ThresholdsChanged;
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125 | if (listener != null) listener(this, e);
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126 | }
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127 | #endregion
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128 |
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129 | public abstract IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData);
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130 | public abstract IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData);
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131 | }
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132 | }
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