#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using HeuristicLab.Common; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis { [StorableClass] public class ClassificationPerformanceMeasuresResultCollection : ResultCollection { #region result names protected const string ClassificationPositiveClassNameResultName = "Classification positive class"; protected const string TrainingTruePositiveRateResultName = "True positive rate (training)"; protected const string TrainingTrueNegativeRateResultName = "True negative rate (training)"; protected const string TrainingPositivePredictiveValueResultName = "Positive predictive value (training)"; protected const string TrainingNegativePredictiveValueResultName = "Negative predictive value (training)"; protected const string TrainingFalsePositiveRateResultName = "False positive rate (training)"; protected const string TrainingFalseDiscoveryRateResultName = "False discovery rate (training)"; protected const string TrainingF1ScoreResultName = "F1 score (training)"; protected const string TrainingMatthewsCorrelationResultName = "Matthews Correlation (training)"; protected const string TestTruePositiveRateResultName = "True positive rate (test)"; protected const string TestTrueNegativeRateResultName = "True negative rate (test)"; protected const string TestPositivePredictiveValueResultName = "Positive predictive value (test)"; protected const string TestNegativePredictiveValueResultName = "Negative predictive value (test)"; protected const string TestFalsePositiveRateResultName = "False positive rate (test)"; protected const string TestFalseDiscoveryRateResultName = "False discovery rate (test)"; protected const string TestF1ScoreResultName = "F1 score (test)"; protected const string TestMatthewsCorrelationResultName = "Matthews Correlation (test)"; #endregion public ClassificationPerformanceMeasuresResultCollection() : base() { AddMeasures(); } [StorableConstructor] protected ClassificationPerformanceMeasuresResultCollection(bool deserializing) : base(deserializing) { } protected ClassificationPerformanceMeasuresResultCollection(ClassificationPerformanceMeasuresResultCollection original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new ClassificationPerformanceMeasuresResultCollection(this, cloner); } #region result properties public string ClassificationPositiveClassName { get { return ((StringValue)this[ClassificationPositiveClassNameResultName].Value).Value; } set { ((StringValue)this[ClassificationPositiveClassNameResultName].Value).Value = value; } } public double TrainingTruePositiveRate { get { return ((DoubleValue)this[TrainingTruePositiveRateResultName].Value).Value; } set { ((DoubleValue)this[TrainingTruePositiveRateResultName].Value).Value = value; } } public double TrainingTrueNegativeRate { get { return ((DoubleValue)this[TrainingTrueNegativeRateResultName].Value).Value; } set { ((DoubleValue)this[TrainingTrueNegativeRateResultName].Value).Value = value; } } public double TrainingPositivePredictiveValue { get { return ((DoubleValue)this[TrainingPositivePredictiveValueResultName].Value).Value; } set { ((DoubleValue)this[TrainingPositivePredictiveValueResultName].Value).Value = value; } } public double TrainingNegativePredictiveValue { get { return ((DoubleValue)this[TrainingNegativePredictiveValueResultName].Value).Value; } set { ((DoubleValue)this[TrainingNegativePredictiveValueResultName].Value).Value = value; } } public double TrainingFalsePositiveRate { get { return ((DoubleValue)this[TrainingFalsePositiveRateResultName].Value).Value; } set { ((DoubleValue)this[TrainingFalsePositiveRateResultName].Value).Value = value; } } public double TrainingFalseDiscoveryRate { get { return ((DoubleValue)this[TrainingFalseDiscoveryRateResultName].Value).Value; } set { ((DoubleValue)this[TrainingFalseDiscoveryRateResultName].Value).Value = value; } } public double TrainingF1Score { get { return ((DoubleValue)this[TrainingF1ScoreResultName].Value).Value; } set { ((DoubleValue)this[TrainingF1ScoreResultName].Value).Value = value; } } public double TrainingMatthewsCorrelation { get { return ((DoubleValue)this[TrainingMatthewsCorrelationResultName].Value).Value; } set { ((DoubleValue)this[TrainingMatthewsCorrelationResultName].Value).Value = value; } } public double TestTruePositiveRate { get { return ((DoubleValue)this[TestTruePositiveRateResultName].Value).Value; } set { ((DoubleValue)this[TestTruePositiveRateResultName].Value).Value = value; } } public double TestTrueNegativeRate { get { return ((DoubleValue)this[TestTrueNegativeRateResultName].Value).Value; } set { ((DoubleValue)this[TestTrueNegativeRateResultName].Value).Value = value; } } public double TestPositivePredictiveValue { get { return ((DoubleValue)this[TestPositivePredictiveValueResultName].Value).Value; } set { ((DoubleValue)this[TestPositivePredictiveValueResultName].Value).Value = value; } } public double TestNegativePredictiveValue { get { return ((DoubleValue)this[TestNegativePredictiveValueResultName].Value).Value; } set { ((DoubleValue)this[TestNegativePredictiveValueResultName].Value).Value = value; } } public double TestFalsePositiveRate { get { return ((DoubleValue)this[TestFalsePositiveRateResultName].Value).Value; } set { ((DoubleValue)this[TestFalsePositiveRateResultName].Value).Value = value; } } public double TestFalseDiscoveryRate { get { return ((DoubleValue)this[TestFalseDiscoveryRateResultName].Value).Value; } set { ((DoubleValue)this[TestFalseDiscoveryRateResultName].Value).Value = value; } } public double TestF1Score { get { return ((DoubleValue)this[TestF1ScoreResultName].Value).Value; } set { ((DoubleValue)this[TestF1ScoreResultName].Value).Value = value; } } public double TestMatthewsCorrelation { get { return ((DoubleValue)this[TestMatthewsCorrelationResultName].Value).Value; } set { ((DoubleValue)this[TestMatthewsCorrelationResultName].Value).Value = value; } } #endregion protected void AddMeasures() { Add(new Result(ClassificationPositiveClassNameResultName, "The positive class which is used for the performance measure calculations.", new StringValue())); Add(new Result(TrainingTruePositiveRateResultName, "Sensitivity/True positive rate of the model on the training partition\n(TP/(TP+FN)).", new PercentValue())); Add(new Result(TrainingTrueNegativeRateResultName, "Specificity/True negative rate of the model on the training partition\n(TN/(FP+TN)).", new PercentValue())); Add(new Result(TrainingPositivePredictiveValueResultName, "Precision/Positive predictive value of the model on the training partition\n(TP/(TP+FP)).", new PercentValue())); Add(new Result(TrainingNegativePredictiveValueResultName, "Negative predictive value of the model on the training partition\n(TN/(TN+FN)).", new PercentValue())); 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())); 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())); Add(new Result(TrainingF1ScoreResultName, "The F1 score of the model on the training partition.", new DoubleValue())); Add(new Result(TrainingMatthewsCorrelationResultName, "The Matthews correlation value of the model on the training partition.", new DoubleValue())); Add(new Result(TestTruePositiveRateResultName, "Sensitivity/True positive rate of the model on the test partition\n(TP/(TP+FN)).", new PercentValue())); Add(new Result(TestTrueNegativeRateResultName, "Specificity/True negative rate of the model on the test partition\n(TN/(FP+TN)).", new PercentValue())); Add(new Result(TestPositivePredictiveValueResultName, "Precision/Positive predictive value of the model on the test partition\n(TP/(TP+FP)).", new PercentValue())); Add(new Result(TestNegativePredictiveValueResultName, "Negative predictive value of the model on the test partition\n(TN/(TN+FN)).", new PercentValue())); 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())); 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())); Add(new Result(TestF1ScoreResultName, "The F1 score of the model on the test partition.", new DoubleValue())); Add(new Result(TestMatthewsCorrelationResultName, "The Matthews correlation value of the model on the test partition.", new DoubleValue())); TrainingTruePositiveRate = double.NaN; TrainingTrueNegativeRate = double.NaN; TrainingPositivePredictiveValue = double.NaN; TrainingNegativePredictiveValue = double.NaN; TrainingFalsePositiveRate = double.NaN; TrainingFalseDiscoveryRate = double.NaN; TrainingF1Score = double.NaN; TrainingMatthewsCorrelation = double.NaN; TestTruePositiveRate = double.NaN; TestTrueNegativeRate = double.NaN; TestPositivePredictiveValue = double.NaN; TestNegativePredictiveValue = double.NaN; TestFalsePositiveRate = double.NaN; TestFalseDiscoveryRate = double.NaN; TestF1Score = double.NaN; TestMatthewsCorrelation = double.NaN; } public void SetTrainingResults(ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator) { if (!string.IsNullOrWhiteSpace(ClassificationPositiveClassName) && !ClassificationPositiveClassName.Equals(trainingPerformanceCalculator.PositiveClassName)) throw new ArgumentException("Classification positive class of the training data doesn't match with the data of test partition."); ClassificationPositiveClassName = trainingPerformanceCalculator.PositiveClassName; TrainingTruePositiveRate = trainingPerformanceCalculator.TruePositiveRate; TrainingTrueNegativeRate = trainingPerformanceCalculator.TrueNegativeRate; TrainingPositivePredictiveValue = trainingPerformanceCalculator.PositivePredictiveValue; TrainingNegativePredictiveValue = trainingPerformanceCalculator.NegativePredictiveValue; TrainingFalsePositiveRate = trainingPerformanceCalculator.FalsePositiveRate; TrainingFalseDiscoveryRate = trainingPerformanceCalculator.FalseDiscoveryRate; } public void SetTestResults(ClassificationPerformanceMeasuresCalculator testPerformanceCalculator) { if (!string.IsNullOrWhiteSpace(ClassificationPositiveClassName) && !ClassificationPositiveClassName.Equals(testPerformanceCalculator.PositiveClassName)) throw new ArgumentException("Classification positive class of the test data doesn't match with the data of training partition."); ClassificationPositiveClassName = testPerformanceCalculator.PositiveClassName; TestTruePositiveRate = testPerformanceCalculator.TruePositiveRate; TestTrueNegativeRate = testPerformanceCalculator.TrueNegativeRate; TestPositivePredictiveValue = testPerformanceCalculator.PositivePredictiveValue; TestNegativePredictiveValue = testPerformanceCalculator.NegativePredictiveValue; TestFalsePositiveRate = testPerformanceCalculator.FalsePositiveRate; TestFalseDiscoveryRate = testPerformanceCalculator.FalseDiscoveryRate; } } }