1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 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 System.Text;
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26 | using HeuristicLab.Common;
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27 |
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28 | namespace HeuristicLab.Problems.DataAnalysis {
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29 | public class ClassificationPerformanceMeasuresCalculator {
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30 |
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31 | public ClassificationPerformanceMeasuresCalculator(string positiveClassName, double positiveClassValue) {
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32 | this.positiveClassName = positiveClassName;
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33 | this.positiveClassValue = positiveClassValue;
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34 | Reset();
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35 | }
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36 |
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37 | #region Properties
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38 | private string positiveClassName;
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39 | private double positiveClassValue;
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40 | private int truePositiveCount, falsePositiveCount, trueNegativeCount, falseNegativeCount;
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41 |
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42 | public string PositiveClassName {
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43 | get {
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44 | return positiveClassName;
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45 | }
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46 | }
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47 | public double PositiveClassValue {
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48 | get {
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49 | return positiveClassValue;
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50 | }
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51 | }
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52 | public double TruePositiveRate {
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53 | get {
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54 | double divisor = truePositiveCount + falseNegativeCount;
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55 | return divisor.IsAlmost(0) ? double.NaN : truePositiveCount / divisor;
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56 | }
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57 | }
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58 | public double TrueNegativeRate {
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59 | get {
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60 | double divisor = falsePositiveCount + trueNegativeCount;
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61 | return divisor.IsAlmost(0) ? double.NaN : trueNegativeCount / divisor;
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62 | }
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63 | }
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64 | public double PositivePredictiveValue {
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65 | get {
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66 | double divisor = truePositiveCount + falsePositiveCount;
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67 | return divisor.IsAlmost(0) ? double.NaN : truePositiveCount / divisor;
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68 | }
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69 | }
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70 | public double NegativePredictiveValue {
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71 | get {
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72 | double divisor = trueNegativeCount + falseNegativeCount;
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73 | return divisor.IsAlmost(0) ? double.NaN : trueNegativeCount / divisor;
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74 | }
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75 | }
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76 | public double FalsePositiveRate {
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77 | get {
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78 | double divisor = falsePositiveCount + trueNegativeCount;
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79 | return divisor.IsAlmost(0) ? double.NaN : falsePositiveCount / divisor;
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80 | }
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81 | }
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82 | public double FalseDiscoveryRate {
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83 | get {
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84 | double divisor = falsePositiveCount + truePositiveCount;
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85 | return divisor.IsAlmost(0) ? double.NaN : falsePositiveCount / divisor;
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86 | }
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87 | }
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88 |
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89 | private OnlineCalculatorError errorState;
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90 | public OnlineCalculatorError ErrorState {
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91 | get { return errorState; }
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92 | }
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93 | #endregion
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94 |
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95 | public void Reset() {
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96 | truePositiveCount = 0;
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97 | falseNegativeCount = 0;
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98 | trueNegativeCount = 0;
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99 | falseNegativeCount = 0;
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100 | errorState = OnlineCalculatorError.InsufficientElementsAdded;
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101 | }
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102 |
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103 | public void Add(double originalClassValue, double estimatedClassValue) {
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104 | // ignore cases where original is NaN completely
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105 | if (!double.IsNaN(originalClassValue)) {
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106 | if (originalClassValue.IsAlmost(positiveClassValue)
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107 | || estimatedClassValue.IsAlmost(positiveClassValue)) { //positive class/positive class estimation
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108 | if (estimatedClassValue.IsAlmost(originalClassValue)) {
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109 | truePositiveCount++;
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110 | } else {
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111 | if (estimatedClassValue.IsAlmost(positiveClassValue)) //misclassification of the negative class
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112 | falsePositiveCount++;
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113 | else //misclassification of the positive class
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114 | falseNegativeCount++;
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115 | }
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116 | } else { //negative class/negative class estimation
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117 | //In a multiclass classification all misclassifications of the negative class
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118 | //will be treated as true negatives except on positive class estimations
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119 | trueNegativeCount++;
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120 | }
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121 | errorState = OnlineCalculatorError.None; // number of (non-NaN) samples >= 1
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122 | }
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123 | }
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124 |
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125 | public void Calculate(IEnumerable<double> originalClassValues, IEnumerable<double> estimatedClassValues,
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126 | out OnlineCalculatorError errorState) {
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127 | IEnumerator<double> originalEnumerator = originalClassValues.GetEnumerator();
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128 | IEnumerator<double> estimatedEnumerator = estimatedClassValues.GetEnumerator();
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129 |
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130 | // always move forward both enumerators (do not use short-circuit evaluation!)
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131 | while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
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132 | double original = originalEnumerator.Current;
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133 | double estimated = estimatedEnumerator.Current;
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134 | Add(original, estimated);
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135 | if (ErrorState != OnlineCalculatorError.None) break;
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136 | }
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137 |
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138 | // check if both enumerators are at the end to make sure both enumerations have the same length
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139 | if (ErrorState == OnlineCalculatorError.None &&
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140 | (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) {
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141 | throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
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142 | } else {
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143 | errorState = ErrorState;
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144 | }
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145 | }
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146 | }
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147 | }
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