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 |
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21 | #endregion
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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.ComponentModel;
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26 | using System.Linq;
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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.Persistence.Default.CompositeSerializers.Storable;
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31 | using HeuristicLab.PluginInfrastructure;
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32 |
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33 | namespace HeuristicLab.Problems.DataAnalysis {
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34 | [StorableClass]
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35 | [Item("FeatureCorrelation", "Represents the correlation of features in a data set.")]
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36 | public class FeatureCorrelation : HeatMap {
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37 |
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38 | private const string PearsonsR = "Pearsons R";
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39 | private const string PearsonsRSquared = "Pearsons R Squared";
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40 | private const string HoeffdingsDependence = "Hoeffdings Dependence";
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41 | private const string SpearmansRank = "Spearmans Rank";
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42 | public IEnumerable<string> CorrelationCalculators {
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43 | get { return new List<string>() { PearsonsR, PearsonsRSquared, HoeffdingsDependence, SpearmansRank }; }
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44 | }
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45 |
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46 | private const string AllSamples = "All Samples";
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47 | private const string TrainingSamples = "Training Samples";
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48 | private const string TestSamples = "Test Samples";
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49 | public IEnumerable<string> Partitions {
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50 | get { return new List<string>() { AllSamples, TrainingSamples, TestSamples }; }
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51 | }
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52 |
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53 | private IDataAnalysisProblemData problemData;
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54 | public IDataAnalysisProblemData ProblemData {
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55 | get { return problemData; }
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56 | set {
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57 | if (problemData != value) {
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58 | problemData = value;
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59 | columnNames = value.Dataset.DoubleVariables.ToList();
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60 | rowNames = value.Dataset.DoubleVariables.ToList();
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61 | OnProblemDataChanged();
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62 | }
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63 | }
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64 | }
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65 |
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66 | private BackgroundWorker bw;
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67 |
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68 | public FeatureCorrelation()
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69 | : base() {
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70 | this.Title = "Feature Correlation";
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71 | this.columnNames = Enumerable.Range(1, 2).Select(x => x.ToString()).ToList();
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72 | this.rowNames = Enumerable.Range(1, 2).Select(x => x.ToString()).ToList();
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73 | sortableView = true;
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74 | }
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75 |
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76 | public FeatureCorrelation(IDataAnalysisProblemData problemData) {
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77 | this.problemData = problemData;
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78 | this.Title = "Feature Correlation";
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79 | this.columnNames = problemData.Dataset.DoubleVariables.ToList();
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80 | this.rowNames = problemData.Dataset.DoubleVariables.ToList();
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81 | sortableView = true;
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82 |
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83 | CalculateElements(problemData.Dataset);
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84 | }
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85 | protected FeatureCorrelation(FeatureCorrelation original, Cloner cloner)
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86 | : base(original, cloner) {
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87 | this.Title = "Feature Correlation";
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88 | this.problemData = original.problemData;
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89 | this.columnNames = original.problemData.Dataset.DoubleVariables.ToList();
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90 | this.rowNames = original.problemData.Dataset.DoubleVariables.ToList();
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91 | }
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92 | public override IDeepCloneable Clone(Cloner cloner) {
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93 | return new FeatureCorrelation(this, cloner);
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94 | }
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95 |
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96 | public void Recalculate(string calc, string partition) {
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97 | CalculateElements(problemData.Dataset, calc, partition);
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98 | }
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99 |
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100 | private void CalculateElements(Dataset dataset) {
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101 | CalculateElements(dataset, CorrelationCalculators.First(), Partitions.First());
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102 | }
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103 |
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104 | private void CalculateElements(Dataset dataset, string calc, string partition) {
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105 | if (bw == null || bw.IsBusy) {
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106 | if (bw != null) {
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107 | bw.CancelAsync();
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108 | }
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109 | bw = new BackgroundWorker();
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110 | bw.WorkerReportsProgress = true;
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111 | bw.WorkerSupportsCancellation = true;
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112 | bw.DoWork += new DoWorkEventHandler(bw_DoWork);
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113 | bw.ProgressChanged += new ProgressChangedEventHandler(bw_ProgressChanged);
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114 | bw.RunWorkerCompleted += new RunWorkerCompletedEventHandler(bw_RunWorkerCompleted);
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115 | }
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116 | bw.RunWorkerAsync(new BackgroundWorkerInfo { Dataset = dataset, Calculator = calc, Partition = partition });
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117 | if (calc.Equals(PearsonsR) || calc.Equals(SpearmansRank)) {
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118 | Maximum = 1.0;
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119 | Minimum = -1.0;
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120 | } else if (calc.Equals(HoeffdingsDependence)) {
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121 | Maximum = 1.0;
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122 | Minimum = -0.5;
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123 | } else {
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124 | Maximum = 1.0;
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125 | Minimum = 0.0;
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126 | }
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127 | }
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128 |
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129 | private void bw_DoWork(object sender, DoWorkEventArgs e) {
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130 | BackgroundWorker worker = sender as BackgroundWorker;
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131 |
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132 | BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
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133 | Dataset dataset = bwInfo.Dataset;
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134 | string partition = bwInfo.Partition;
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135 | string calc = bwInfo.Calculator;
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136 |
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137 | IList<string> doubleVariableNames = dataset.DoubleVariables.ToList();
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138 | OnlineCalculatorError error;
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139 | int length = doubleVariableNames.Count;
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140 | double[,] elements = new double[length, length];
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141 |
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142 | double calculations = (Math.Pow(length, 2) + length) / 2;
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143 |
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144 | worker.ReportProgress(0);
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145 |
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146 | for (int i = 0; i < length; i++) {
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147 | for (int j = 0; j < i + 1; j++) {
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148 | if (worker.CancellationPending) {
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149 | e.Cancel = true;
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150 | return;
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151 | }
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152 |
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153 | IEnumerable<double> var1 = dataset.GetDoubleValues(doubleVariableNames[i]);
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154 | IEnumerable<double> var2 = dataset.GetDoubleValues(doubleVariableNames[j]);
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155 | if (partition.Equals(TrainingSamples)) {
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156 | var1 = var1.Skip(problemData.TrainingPartition.Start).Take(problemData.TrainingPartition.End - problemData.TrainingPartition.Start);
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157 | var2 = var2.Skip(problemData.TrainingPartition.Start).Take(problemData.TrainingPartition.End - problemData.TrainingPartition.Start);
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158 | } else if (partition.Equals(TestSamples)) {
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159 | var1 = var1.Skip(problemData.TestPartition.Start).Take(problemData.TestPartition.End - problemData.TestPartition.Start);
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160 | var2 = var2.Skip(problemData.TestPartition.Start).Take(problemData.TestPartition.End - problemData.TestPartition.Start);
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161 | }
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162 |
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163 | if (calc.Equals(HoeffdingsDependence)) {
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164 | elements[i, j] = HoeffdingsDependenceCalculator.Calculate(var1, var2, out error);
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165 | } else if (calc.Equals(SpearmansRank)) {
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166 | elements[i, j] = SpearmansRankCorrelationCoefficientCalculator.Calculate(var1, var2, out error);
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167 | } else if (calc.Equals(PearsonsRSquared)) {
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168 | elements[i, j] = OnlinePearsonsRSquaredCalculator.Calculate(var1, var2, out error);
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169 | } else {
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170 | elements[i, j] = OnlinePearsonsRSquaredCalculator.CalculateR(var1, var2, out error);
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171 | }
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172 | elements[j, i] = elements[i, j];
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173 | if (!error.Equals(OnlineCalculatorError.None)) {
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174 | worker.ReportProgress(100);
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175 | throw new ArgumentException("Calculator returned " + error + Environment.NewLine + "Maybe try another calculator.");
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176 | }
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177 | worker.ReportProgress((int)Math.Round((((Math.Pow(i, 2) + i) / 2 + j + 1.0) / calculations) * 100));
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178 | }
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179 | }
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180 | e.Result = elements;
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181 | }
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182 |
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183 | private void bw_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e) {
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184 | BackgroundWorker worker = sender as BackgroundWorker;
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185 | if (!e.Cancelled && !worker.CancellationPending) {
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186 | if (!(e.Error == null)) {
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187 | ErrorHandling.ShowErrorDialog(e.Error);
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188 | } else {
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189 | matrix = (double[,])e.Result;
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190 | OnReset();
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191 | }
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192 | }
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193 | }
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194 |
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195 | #region events
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196 | public delegate void ProgressCalculationHandler(object sender, ProgressChangedEventArgs e);
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197 | public event ProgressCalculationHandler ProgressCalculation;
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198 | protected void bw_ProgressChanged(object sender, ProgressChangedEventArgs e) {
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199 | BackgroundWorker worker = sender as BackgroundWorker;
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200 | if (!worker.CancellationPending && ProgressCalculation != null) {
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201 | ProgressCalculation(sender, e);
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202 | }
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203 | }
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204 |
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205 | public event EventHandler ProblemDataChanged;
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206 | protected virtual void OnProblemDataChanged() {
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207 | EventHandler handler = ProblemDataChanged;
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208 | if (handler != null)
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209 | handler(this, EventArgs.Empty);
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210 | }
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211 | #endregion
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212 |
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213 | protected class BackgroundWorkerInfo {
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214 | public Dataset Dataset { get; set; }
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215 | public string Calculator { get; set; }
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216 | public string Partition { get; set; }
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217 | }
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218 | }
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219 | }
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