#region License Information /* HeuristicLab * Copyright (C) 2002-2011 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 System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis { /// /// Abstract base class for classification data analysis solutions /// [StorableClass] public abstract class ClassificationSolution : DataAnalysisSolution, IClassificationSolution { private const string TrainingAccuracyResultName = "Accuracy (training)"; private const string TestAccuracyResultName = "Accuracy (test)"; public new IClassificationModel Model { get { return (IClassificationModel)base.Model; } protected set { base.Model = value; } } public new IClassificationProblemData ProblemData { get { return (IClassificationProblemData)base.ProblemData; } protected set { base.ProblemData = value; } } 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; } } [StorableConstructor] protected ClassificationSolution(bool deserializing) : base(deserializing) { } protected ClassificationSolution(ClassificationSolution original, Cloner cloner) : base(original, cloner) { } public ClassificationSolution(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())); RecalculateResults(); } protected override void OnProblemDataChanged(EventArgs e) { base.OnProblemDataChanged(e); RecalculateResults(); } protected override void OnModelChanged(EventArgs e) { base.OnModelChanged(e); RecalculateResults(); } protected void RecalculateResults() { double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values IEnumerable originalTrainingClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes); double[] estimatedTestClassValues = EstimatedTestClassValues.ToArray(); // cache values IEnumerable originalTestClassValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes); OnlineCalculatorError errorState; double trainingAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTrainingClassValues, originalTrainingClassValues, out errorState); if (errorState != OnlineCalculatorError.None) trainingAccuracy = double.NaN; double testAccuracy = OnlineAccuracyCalculator.Calculate(estimatedTestClassValues, originalTestClassValues, out errorState); if (errorState != OnlineCalculatorError.None) testAccuracy = double.NaN; TrainingAccuracy = trainingAccuracy; TestAccuracy = testAccuracy; } public virtual IEnumerable EstimatedClassValues { get { return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); } } public virtual IEnumerable EstimatedTrainingClassValues { get { return GetEstimatedClassValues(ProblemData.TrainingIndizes); } } public virtual IEnumerable EstimatedTestClassValues { get { return GetEstimatedClassValues(ProblemData.TestIndizes); } } public virtual IEnumerable GetEstimatedClassValues(IEnumerable rows) { return Model.GetEstimatedClassValues(ProblemData.Dataset, rows); } } }