#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.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis.OnlineCalculators; namespace HeuristicLab.Problems.DataAnalysis { [StorableClass] public abstract class ClassificationSolutionBase : DataAnalysisSolution, IClassificationSolution { private const string TrainingAccuracyResultName = "Accuracy (training)"; private const string TestAccuracyResultName = "Accuracy (test)"; private const string TrainingNormalizedGiniCoefficientResultName = "Normalized Gini Coefficient (training)"; private const string TestNormalizedGiniCoefficientResultName = "Normalized Gini Coefficient (test)"; private const string ClassificationPerformanceMeasuresResultName = "Classification Performance Measures"; public new IClassificationModel Model { get { return (IClassificationModel)base.Model; } protected set { base.Model = value; } } public new IClassificationProblemData ProblemData { get { return (IClassificationProblemData)base.ProblemData; } set { base.ProblemData = value; } } #region Results public double TrainingAccuracy { get { return ((DoubleValue)this[TrainingAccuracyResultName].Value).Value; } private set { ((DoubleValue)this[TrainingAccuracyResultName].Value).Value = value; } } public double TestAccuracy { get { return ((DoubleValue)this[TestAccuracyResultName].Value).Value; } private set { ((DoubleValue)this[TestAccuracyResultName].Value).Value = value; } } public double TrainingNormalizedGiniCoefficient { get { return ((DoubleValue)this[TrainingNormalizedGiniCoefficientResultName].Value).Value; } protected set { ((DoubleValue)this[TrainingNormalizedGiniCoefficientResultName].Value).Value = value; } } public double TestNormalizedGiniCoefficient { get { return ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value; } protected set { ((DoubleValue)this[TestNormalizedGiniCoefficientResultName].Value).Value = value; } } public ClassificationPerformanceMeasuresResultCollection ClassificationPerformanceMeasures { get { return ((ClassificationPerformanceMeasuresResultCollection)this[ClassificationPerformanceMeasuresResultName].Value); } protected set { (this[ClassificationPerformanceMeasuresResultName].Value) = value; } } #endregion [StorableConstructor] protected ClassificationSolutionBase(bool deserializing) : base(deserializing) { } protected ClassificationSolutionBase(ClassificationSolutionBase original, Cloner cloner) : base(original, cloner) { } protected ClassificationSolutionBase(IClassificationModel model, IClassificationProblemData problemData) : base(model, problemData) { Add(new Result(TrainingAccuracyResultName, "Accuracy of the model on the training partition (percentage of correctly classified instances).", new PercentValue())); Add(new Result(TestAccuracyResultName, "Accuracy of the model on the test partition (percentage of correctly classified instances).", new PercentValue())); Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the training partition.", new DoubleValue())); Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the test partition.", new DoubleValue())); Add(new Result(ClassificationPerformanceMeasuresResultName, @"Classification performance measures.\n In a multiclass classification all misclassifications of the negative class will be treated as true negatives except on positive class estimations.", new ClassificationPerformanceMeasuresResultCollection())); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { if (!this.ContainsKey(TrainingNormalizedGiniCoefficientResultName)) Add(new Result(TrainingNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the training partition.", new DoubleValue())); if (!this.ContainsKey(TestNormalizedGiniCoefficientResultName)) Add(new Result(TestNormalizedGiniCoefficientResultName, "Normalized Gini coefficient of the model on the test partition.", new DoubleValue())); if (!this.ContainsKey(ClassificationPerformanceMeasuresResultName)) { Add(new Result(ClassificationPerformanceMeasuresResultName, @"Classification performance measures.\n In a multiclass classification all misclassifications of the negative class will be treated as true negatives except on positive class estimations.", new ClassificationPerformanceMeasuresResultCollection())); CalculateClassificationResults(); } } protected void CalculateClassificationResults() { double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values double[] originalTrainingClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray(); double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values double[] originalTestClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToArray(); var positiveClassName = ProblemData.PositiveClass; double positiveClassValue = ProblemData.GetClassValue(positiveClassName); ClassificationPerformanceMeasuresCalculator trainingPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue); ClassificationPerformanceMeasuresCalculator testPerformanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, positiveClassValue); OnlineCalculatorError errorState; double trainingAccuracy = OnlineAccuracyCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState); if (errorState != OnlineCalculatorError.None) trainingAccuracy = double.NaN; double testAccuracy = OnlineAccuracyCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState); if (errorState != OnlineCalculatorError.None) testAccuracy = double.NaN; TrainingAccuracy = trainingAccuracy; TestAccuracy = testAccuracy; double trainingNormalizedGini = NormalizedGiniCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState); if (errorState != OnlineCalculatorError.None) trainingNormalizedGini = double.NaN; double testNormalizedGini = NormalizedGiniCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState); if (errorState != OnlineCalculatorError.None) testNormalizedGini = double.NaN; TrainingNormalizedGiniCoefficient = trainingNormalizedGini; TestNormalizedGiniCoefficient = testNormalizedGini; ClassificationPerformanceMeasures.Reset(); trainingPerformanceCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues); if (trainingPerformanceCalculator.ErrorState == OnlineCalculatorError.None) ClassificationPerformanceMeasures.SetTrainingResults(trainingPerformanceCalculator); testPerformanceCalculator.Calculate(originalTestClassValues, estimatedTestClassValues); if (testPerformanceCalculator.ErrorState == OnlineCalculatorError.None) ClassificationPerformanceMeasures.SetTestResults(testPerformanceCalculator); if (ProblemData.Classes == 2) { var f1Training = FOneScoreCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState); if (errorState == OnlineCalculatorError.None) ClassificationPerformanceMeasures.TrainingF1Score = f1Training; var f1Test = FOneScoreCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState); if (errorState == OnlineCalculatorError.None) ClassificationPerformanceMeasures.TestF1Score = f1Test; } var mccTraining = MatthewsCorrelationCoefficientCalculator.Calculate(originalTrainingClassValues, estimatedTrainingClassValues, out errorState); if (errorState == OnlineCalculatorError.None) ClassificationPerformanceMeasures.TrainingMatthewsCorrelation = mccTraining; var mccTest = MatthewsCorrelationCoefficientCalculator.Calculate(originalTestClassValues, estimatedTestClassValues, out errorState); if (errorState == OnlineCalculatorError.None) ClassificationPerformanceMeasures.TestMatthewsCorrelation = mccTest; } public abstract IEnumerable EstimatedClassValues { get; } public abstract IEnumerable EstimatedTrainingClassValues { get; } public abstract IEnumerable EstimatedTestClassValues { get; } public abstract IEnumerable GetEstimatedClassValues(IEnumerable rows); protected override void RecalculateResults() { CalculateClassificationResults(); } } }