[11685] | 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 HeuristicLab.Data;
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| 24 | using HeuristicLab.Optimization;
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| 25 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 26 |
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| 27 | namespace HeuristicLab.Problems.DataAnalysis {
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| 28 | [StorableClass]
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| 29 | public class ClassificationPerformanceMeasuresResultCollection : ResultCollection {
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| 30 | #region result names
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| 31 | protected const string ClassificationPositiveClassNameResultName = "Classification positive class";
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| 32 | protected const string TrainingTruePositiveRateResultName = "True positive rate (training)";
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| 33 | protected const string TrainingTrueNegativeRateResultName = "True negative rate (training)";
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| 34 | protected const string TrainingPositivePredictiveValueResultName = "Positive predictive value (training)";
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| 35 | protected const string TrainingNegativePredictiveValueResultName = "Negative predictive value (training)";
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| 36 | protected const string TrainingFalsePositiveRateResultName = "False positive rate (training)";
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| 37 | protected const string TrainingFalseDiscoveryRateResultName = "False discovery rate (training)";
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| 38 | protected const string TestTruePositiveRateResultName = "True positive rate (test)";
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| 39 | protected const string TestTrueNegativeRateResultName = "True negative rate (test)";
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| 40 | protected const string TestPositivePredictiveValueResultName = "Positive predictive value (test)";
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| 41 | protected const string TestNegativePredictiveValueResultName = "Negative predictive value (test)";
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| 42 | protected const string TestFalsePositiveRateResultName = "False positive rate (test)";
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| 43 | protected const string TestFalseDiscoveryRateResultName = "False discovery rate (test)";
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| 44 | #endregion
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| 45 |
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| 46 | public ClassificationPerformanceMeasuresResultCollection()
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| 47 | : base() {
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| 48 | AddMeasures();
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| 49 | }
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| 50 | [StorableConstructor]
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| 51 | protected ClassificationPerformanceMeasuresResultCollection(bool deserializing)
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| 52 | : base(deserializing) {
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| 53 | }
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| 54 |
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| 55 | #region result properties
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| 56 | public string ClassificationPositiveClassName {
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| 57 | get { return ((StringValue)this[ClassificationPositiveClassNameResultName].Value).Value; }
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| 58 | set { ((StringValue)this[ClassificationPositiveClassNameResultName].Value).Value = value; }
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| 59 | }
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| 60 | public double TrainingTruePositiveRate {
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| 61 | get { return ((DoubleValue)this[TrainingTruePositiveRateResultName].Value).Value; }
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| 62 | set { ((DoubleValue)this[TrainingTruePositiveRateResultName].Value).Value = value; }
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| 63 | }
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| 64 | public double TrainingTrueNegativeRate {
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| 65 | get { return ((DoubleValue)this[TrainingTrueNegativeRateResultName].Value).Value; }
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| 66 | set { ((DoubleValue)this[TrainingTrueNegativeRateResultName].Value).Value = value; }
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| 67 | }
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| 68 | public double TrainingPositivePredictiveValue {
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| 69 | get { return ((DoubleValue)this[TrainingPositivePredictiveValueResultName].Value).Value; }
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| 70 | set { ((DoubleValue)this[TrainingPositivePredictiveValueResultName].Value).Value = value; }
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| 71 | }
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| 72 | public double TrainingNegativePredictiveValue {
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| 73 | get { return ((DoubleValue)this[TrainingNegativePredictiveValueResultName].Value).Value; }
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| 74 | set { ((DoubleValue)this[TrainingNegativePredictiveValueResultName].Value).Value = value; }
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| 75 | }
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| 76 | public double TrainingFalsePositiveRate {
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| 77 | get { return ((DoubleValue)this[TrainingFalsePositiveRateResultName].Value).Value; }
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| 78 | set { ((DoubleValue)this[TrainingFalsePositiveRateResultName].Value).Value = value; }
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| 79 | }
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| 80 | public double TrainingFalseDiscoveryRate {
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| 81 | get { return ((DoubleValue)this[TrainingFalseDiscoveryRateResultName].Value).Value; }
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| 82 | set { ((DoubleValue)this[TrainingFalseDiscoveryRateResultName].Value).Value = value; }
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| 83 | }
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| 84 | public double TestTruePositiveRate {
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| 85 | get { return ((DoubleValue)this[TestTruePositiveRateResultName].Value).Value; }
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| 86 | set { ((DoubleValue)this[TestTruePositiveRateResultName].Value).Value = value; }
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| 87 | }
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| 88 | public double TestTrueNegativeRate {
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| 89 | get { return ((DoubleValue)this[TestTrueNegativeRateResultName].Value).Value; }
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| 90 | set { ((DoubleValue)this[TestTrueNegativeRateResultName].Value).Value = value; }
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| 91 | }
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| 92 | public double TestPositivePredictiveValue {
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| 93 | get { return ((DoubleValue)this[TestPositivePredictiveValueResultName].Value).Value; }
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| 94 | set { ((DoubleValue)this[TestPositivePredictiveValueResultName].Value).Value = value; }
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| 95 | }
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| 96 | public double TestNegativePredictiveValue {
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| 97 | get { return ((DoubleValue)this[TestNegativePredictiveValueResultName].Value).Value; }
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| 98 | set { ((DoubleValue)this[TestNegativePredictiveValueResultName].Value).Value = value; }
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| 99 | }
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| 100 | public double TestFalsePositiveRate {
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| 101 | get { return ((DoubleValue)this[TestFalsePositiveRateResultName].Value).Value; }
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| 102 | set { ((DoubleValue)this[TestFalsePositiveRateResultName].Value).Value = value; }
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| 103 | }
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| 104 | public double TestFalseDiscoveryRate {
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| 105 | get { return ((DoubleValue)this[TestFalseDiscoveryRateResultName].Value).Value; }
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| 106 | set { ((DoubleValue)this[TestFalseDiscoveryRateResultName].Value).Value = value; }
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| 107 | }
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| 108 | #endregion
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| 109 |
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| 110 | protected void AddMeasures() {
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| 111 | Add(new Result(ClassificationPositiveClassNameResultName, "The positive class which is used for the performance measure calculations.", new StringValue()));
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| 112 | Add(new Result(TrainingTruePositiveRateResultName, "Sensitivity/True positive rate of the model on the training partition\n(TP/(TP+FN)).", new PercentValue()));
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| 113 | Add(new Result(TrainingTrueNegativeRateResultName, "Specificity/True negative rate of the model on the training partition\n(TN/(FP+TN)).", new PercentValue()));
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| 114 | Add(new Result(TrainingPositivePredictiveValueResultName, "Precision/Positive predictive value of the model on the training partition\n(TP/(TP+FP)).", new PercentValue()));
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| 115 | Add(new Result(TrainingNegativePredictiveValueResultName, "Negative predictive value of the model on the training partition\n(TN/(TN+FN)).", new PercentValue()));
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| 116 | Add(new Result(TrainingFalsePositiveRateResultName, "The false positive rate is the complement of the true negative rate of the model on the training partition.", new PercentValue()));
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| 117 | Add(new Result(TrainingFalseDiscoveryRateResultName, "The false discovery rate is the complement of the positive predictive value of the model on the training partition.", new PercentValue()));
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| 118 | Add(new Result(TestTruePositiveRateResultName, "Sensitivity/True positive rate of the model on the test partition\n(TP/(TP+FN)).", new PercentValue()));
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| 119 | Add(new Result(TestTrueNegativeRateResultName, "Specificity/True negative rate of the model on the test partition\n(TN/(FP+TN)).", new PercentValue()));
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| 120 | Add(new Result(TestPositivePredictiveValueResultName, "Precision/Positive predictive value of the model on the test partition\n(TP/(TP+FP)).", new PercentValue()));
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| 121 | Add(new Result(TestNegativePredictiveValueResultName, "Negative predictive value of the model on the test partition\n(TN/(TN+FN)).", new PercentValue()));
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| 122 | Add(new Result(TestFalsePositiveRateResultName, "The false positive rate is the complement of the true negative rate of the model on the test partition.", new PercentValue()));
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| 123 | Add(new Result(TestFalseDiscoveryRateResultName, "The false discovery rate is the complement of the positive predictive value of the model on the test partition.", new PercentValue()));
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| 124 | TrainingTruePositiveRate = double.NaN;
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| 125 | TrainingTrueNegativeRate = double.NaN;
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| 126 | TrainingPositivePredictiveValue = double.NaN;
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| 127 | TrainingNegativePredictiveValue = double.NaN;
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| 128 | TrainingFalsePositiveRate = double.NaN;
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| 129 | TrainingFalseDiscoveryRate = double.NaN;
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| 130 | TestTruePositiveRate = double.NaN;
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| 131 | TestTrueNegativeRate = double.NaN;
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| 132 | TestPositivePredictiveValue = double.NaN;
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| 133 | TestNegativePredictiveValue = double.NaN;
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| 134 | TestFalsePositiveRate = double.NaN;
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| 135 | TestFalseDiscoveryRate = double.NaN;
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| 136 | }
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| 137 |
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| 138 | public void SetTrainingResults(ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator) {
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| 139 | if (!string.IsNullOrWhiteSpace(ClassificationPositiveClassName)
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| 140 | && !ClassificationPositiveClassName.Equals(trainingPerformanceCalculator.PositiveClassName))
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[11686] | 141 | throw new ArgumentException("Classification positive class of the training data doesn't match with the data of test partition.");
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[11685] | 142 | ClassificationPositiveClassName = trainingPerformanceCalculator.PositiveClassName;
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| 143 | TrainingTruePositiveRate = trainingPerformanceCalculator.TruePositiveRate;
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| 144 | TrainingTrueNegativeRate = trainingPerformanceCalculator.TrueNegativeRate;
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| 145 | TrainingPositivePredictiveValue = trainingPerformanceCalculator.PositivePredictiveValue;
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| 146 | TrainingNegativePredictiveValue = trainingPerformanceCalculator.NegativePredictiveValue;
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| 147 | TrainingFalsePositiveRate = trainingPerformanceCalculator.FalsePositiveRate;
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| 148 | TrainingFalseDiscoveryRate = trainingPerformanceCalculator.FalseDiscoveryRate;
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| 149 | }
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| 150 |
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| 151 | public void SetTestResults(ClassificationPerformanceMeasuresCalculator testPerformanceCalculator) {
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| 152 | if (!string.IsNullOrWhiteSpace(ClassificationPositiveClassName)
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| 153 | && !ClassificationPositiveClassName.Equals(testPerformanceCalculator.PositiveClassName))
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[11686] | 154 | throw new ArgumentException("Classification positive class of the test data doesn't match with the data of training partition.");
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[11685] | 155 | ClassificationPositiveClassName = testPerformanceCalculator.PositiveClassName;
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| 156 | TestTruePositiveRate = testPerformanceCalculator.TruePositiveRate;
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| 157 | TestTrueNegativeRate = testPerformanceCalculator.TrueNegativeRate;
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| 158 | TestPositivePredictiveValue = testPerformanceCalculator.PositivePredictiveValue;
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| 159 | TestNegativePredictiveValue = testPerformanceCalculator.NegativePredictiveValue;
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| 160 | TestFalsePositiveRate = testPerformanceCalculator.FalsePositiveRate;
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| 161 | TestFalseDiscoveryRate = testPerformanceCalculator.FalseDiscoveryRate;
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| 162 | }
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| 163 | }
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| 164 | }
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