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source: trunk/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationPerformanceMeasures.cs @ 17389

Last change on this file since 17389 was 17180, checked in by swagner, 5 years ago

#2875: Removed years in copyrights
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using HeuristicLab.Common;
24using HeuristicLab.Data;
25using HeuristicLab.Optimization;
26using HEAL.Attic;
27
28namespace HeuristicLab.Problems.DataAnalysis {
29  [StorableType("6F44E140-22CF-48D3-B100-B6013F2B6608")]
30  public class ClassificationPerformanceMeasuresResultCollection : ResultCollection {
31    #region result names
32    protected const string ClassificationPositiveClassNameResultName = "Classification positive class";
33    protected const string TrainingTruePositiveRateResultName = "True positive rate (training)";
34    protected const string TrainingTrueNegativeRateResultName = "True negative rate (training)";
35    protected const string TrainingPositivePredictiveValueResultName = "Positive predictive value (training)";
36    protected const string TrainingNegativePredictiveValueResultName = "Negative predictive value (training)";
37    protected const string TrainingFalsePositiveRateResultName = "False positive rate (training)";
38    protected const string TrainingFalseDiscoveryRateResultName = "False discovery rate (training)";
39    protected const string TrainingF1ScoreResultName = "F1 score (training)";
40    protected const string TrainingMatthewsCorrelationResultName = "Matthews Correlation (training)";
41    protected const string TestTruePositiveRateResultName = "True positive rate (test)";
42    protected const string TestTrueNegativeRateResultName = "True negative rate (test)";
43    protected const string TestPositivePredictiveValueResultName = "Positive predictive value (test)";
44    protected const string TestNegativePredictiveValueResultName = "Negative predictive value (test)";
45    protected const string TestFalsePositiveRateResultName = "False positive rate (test)";
46    protected const string TestFalseDiscoveryRateResultName = "False discovery rate (test)";
47    protected const string TestF1ScoreResultName = "F1 score (test)";
48    protected const string TestMatthewsCorrelationResultName = "Matthews Correlation (test)";
49    #endregion
50
51    public ClassificationPerformanceMeasuresResultCollection()
52      : base() {
53      AddMeasures();
54    }
55    [StorableConstructor]
56    protected ClassificationPerformanceMeasuresResultCollection(StorableConstructorFlag _) : base(_) {
57    }
58
59    protected ClassificationPerformanceMeasuresResultCollection(ClassificationPerformanceMeasuresResultCollection original, Cloner cloner)
60      : base(original, cloner) { }
61    public override IDeepCloneable Clone(Cloner cloner) {
62      return new ClassificationPerformanceMeasuresResultCollection(this, cloner);
63    }
64
65    #region result properties
66    public string ClassificationPositiveClassName {
67      get { return ((StringValue)this[ClassificationPositiveClassNameResultName].Value).Value; }
68      set { ((StringValue)this[ClassificationPositiveClassNameResultName].Value).Value = value; }
69    }
70    public double TrainingTruePositiveRate {
71      get { return ((DoubleValue)this[TrainingTruePositiveRateResultName].Value).Value; }
72      set { ((DoubleValue)this[TrainingTruePositiveRateResultName].Value).Value = value; }
73    }
74    public double TrainingTrueNegativeRate {
75      get { return ((DoubleValue)this[TrainingTrueNegativeRateResultName].Value).Value; }
76      set { ((DoubleValue)this[TrainingTrueNegativeRateResultName].Value).Value = value; }
77    }
78    public double TrainingPositivePredictiveValue {
79      get { return ((DoubleValue)this[TrainingPositivePredictiveValueResultName].Value).Value; }
80      set { ((DoubleValue)this[TrainingPositivePredictiveValueResultName].Value).Value = value; }
81    }
82    public double TrainingNegativePredictiveValue {
83      get { return ((DoubleValue)this[TrainingNegativePredictiveValueResultName].Value).Value; }
84      set { ((DoubleValue)this[TrainingNegativePredictiveValueResultName].Value).Value = value; }
85    }
86    public double TrainingFalsePositiveRate {
87      get { return ((DoubleValue)this[TrainingFalsePositiveRateResultName].Value).Value; }
88      set { ((DoubleValue)this[TrainingFalsePositiveRateResultName].Value).Value = value; }
89    }
90    public double TrainingFalseDiscoveryRate {
91      get { return ((DoubleValue)this[TrainingFalseDiscoveryRateResultName].Value).Value; }
92      set { ((DoubleValue)this[TrainingFalseDiscoveryRateResultName].Value).Value = value; }
93    }
94    public double TrainingF1Score {
95      get { return ((DoubleValue)this[TrainingF1ScoreResultName].Value).Value; }
96      set { ((DoubleValue)this[TrainingF1ScoreResultName].Value).Value = value; }
97    }
98    public double TrainingMatthewsCorrelation {
99      get { return ((DoubleValue)this[TrainingMatthewsCorrelationResultName].Value).Value; }
100      set { ((DoubleValue)this[TrainingMatthewsCorrelationResultName].Value).Value = value; }
101    }
102    public double TestTruePositiveRate {
103      get { return ((DoubleValue)this[TestTruePositiveRateResultName].Value).Value; }
104      set { ((DoubleValue)this[TestTruePositiveRateResultName].Value).Value = value; }
105    }
106    public double TestTrueNegativeRate {
107      get { return ((DoubleValue)this[TestTrueNegativeRateResultName].Value).Value; }
108      set { ((DoubleValue)this[TestTrueNegativeRateResultName].Value).Value = value; }
109    }
110    public double TestPositivePredictiveValue {
111      get { return ((DoubleValue)this[TestPositivePredictiveValueResultName].Value).Value; }
112      set { ((DoubleValue)this[TestPositivePredictiveValueResultName].Value).Value = value; }
113    }
114    public double TestNegativePredictiveValue {
115      get { return ((DoubleValue)this[TestNegativePredictiveValueResultName].Value).Value; }
116      set { ((DoubleValue)this[TestNegativePredictiveValueResultName].Value).Value = value; }
117    }
118    public double TestFalsePositiveRate {
119      get { return ((DoubleValue)this[TestFalsePositiveRateResultName].Value).Value; }
120      set { ((DoubleValue)this[TestFalsePositiveRateResultName].Value).Value = value; }
121    }
122    public double TestFalseDiscoveryRate {
123      get { return ((DoubleValue)this[TestFalseDiscoveryRateResultName].Value).Value; }
124      set { ((DoubleValue)this[TestFalseDiscoveryRateResultName].Value).Value = value; }
125    }
126    public double TestF1Score {
127      get { return ((DoubleValue)this[TestF1ScoreResultName].Value).Value; }
128      set { ((DoubleValue)this[TestF1ScoreResultName].Value).Value = value; }
129    }
130    public double TestMatthewsCorrelation {
131      get { return ((DoubleValue)this[TestMatthewsCorrelationResultName].Value).Value; }
132      set { ((DoubleValue)this[TestMatthewsCorrelationResultName].Value).Value = value; }
133    }
134    #endregion
135
136    protected void AddMeasures() {
137      Add(new Result(ClassificationPositiveClassNameResultName, "The positive class which is used for the performance measure calculations.", new StringValue()));
138      Add(new Result(TrainingTruePositiveRateResultName, "Sensitivity/True positive rate of the model on the training partition\n(TP/(TP+FN)).", new PercentValue()));
139      Add(new Result(TrainingTrueNegativeRateResultName, "Specificity/True negative rate of the model on the training partition\n(TN/(FP+TN)).", new PercentValue()));
140      Add(new Result(TrainingPositivePredictiveValueResultName, "Precision/Positive predictive value of the model on the training partition\n(TP/(TP+FP)).", new PercentValue()));
141      Add(new Result(TrainingNegativePredictiveValueResultName, "Negative predictive value of the model on the training partition\n(TN/(TN+FN)).", new PercentValue()));
142      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()));
143      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()));
144      Add(new Result(TrainingF1ScoreResultName, "The F1 score of the model on the training partition.", new DoubleValue()));
145      Add(new Result(TrainingMatthewsCorrelationResultName, "The Matthews correlation value of the model on the training partition.", new DoubleValue()));
146      Add(new Result(TestTruePositiveRateResultName, "Sensitivity/True positive rate of the model on the test partition\n(TP/(TP+FN)).", new PercentValue()));
147      Add(new Result(TestTrueNegativeRateResultName, "Specificity/True negative rate of the model on the test partition\n(TN/(FP+TN)).", new PercentValue()));
148      Add(new Result(TestPositivePredictiveValueResultName, "Precision/Positive predictive value of the model on the test partition\n(TP/(TP+FP)).", new PercentValue()));
149      Add(new Result(TestNegativePredictiveValueResultName, "Negative predictive value of the model on the test partition\n(TN/(TN+FN)).", new PercentValue()));
150      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()));
151      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()));
152      Add(new Result(TestF1ScoreResultName, "The F1 score of the model on the test partition.", new DoubleValue()));
153      Add(new Result(TestMatthewsCorrelationResultName, "The Matthews correlation value of the model on the test partition.", new DoubleValue()));
154
155      Reset();
156    }
157
158
159    public void Reset() {
160      TrainingTruePositiveRate = double.NaN;
161      TrainingTrueNegativeRate = double.NaN;
162      TrainingPositivePredictiveValue = double.NaN;
163      TrainingNegativePredictiveValue = double.NaN;
164      TrainingFalsePositiveRate = double.NaN;
165      TrainingFalseDiscoveryRate = double.NaN;
166      TrainingF1Score = double.NaN;
167      TrainingMatthewsCorrelation = double.NaN;
168      TestTruePositiveRate = double.NaN;
169      TestTrueNegativeRate = double.NaN;
170      TestPositivePredictiveValue = double.NaN;
171      TestNegativePredictiveValue = double.NaN;
172      TestFalsePositiveRate = double.NaN;
173      TestFalseDiscoveryRate = double.NaN;
174      TestF1Score = double.NaN;
175      TestMatthewsCorrelation = double.NaN;
176    }
177
178    public void SetTrainingResults(ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator) {
179      if (!string.IsNullOrWhiteSpace(ClassificationPositiveClassName)
180              && !ClassificationPositiveClassName.Equals(trainingPerformanceCalculator.PositiveClassName))
181        throw new ArgumentException("Classification positive class of the training data doesn't match with the data of test partition.");
182      ClassificationPositiveClassName = trainingPerformanceCalculator.PositiveClassName;
183      TrainingTruePositiveRate = trainingPerformanceCalculator.TruePositiveRate;
184      TrainingTrueNegativeRate = trainingPerformanceCalculator.TrueNegativeRate;
185      TrainingPositivePredictiveValue = trainingPerformanceCalculator.PositivePredictiveValue;
186      TrainingNegativePredictiveValue = trainingPerformanceCalculator.NegativePredictiveValue;
187      TrainingFalsePositiveRate = trainingPerformanceCalculator.FalsePositiveRate;
188      TrainingFalseDiscoveryRate = trainingPerformanceCalculator.FalseDiscoveryRate;
189    }
190
191    public void SetTestResults(ClassificationPerformanceMeasuresCalculator testPerformanceCalculator) {
192      if (!string.IsNullOrWhiteSpace(ClassificationPositiveClassName)
193                && !ClassificationPositiveClassName.Equals(testPerformanceCalculator.PositiveClassName))
194        throw new ArgumentException("Classification positive class of the test data doesn't match with the data of training partition.");
195      ClassificationPositiveClassName = testPerformanceCalculator.PositiveClassName;
196      TestTruePositiveRate = testPerformanceCalculator.TruePositiveRate;
197      TestTrueNegativeRate = testPerformanceCalculator.TrueNegativeRate;
198      TestPositivePredictiveValue = testPerformanceCalculator.PositivePredictiveValue;
199      TestNegativePredictiveValue = testPerformanceCalculator.NegativePredictiveValue;
200      TestFalsePositiveRate = testPerformanceCalculator.FalsePositiveRate;
201      TestFalseDiscoveryRate = testPerformanceCalculator.FalseDiscoveryRate;
202    }
203  }
204}
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