#region License Information /* HeuristicLab * Copyright (C) 2002-2019 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 HEAL.Attic; using HeuristicLab.Common; using HeuristicLab.Core; namespace HeuristicLab.Problems.DataAnalysis { /// /// Represents discriminant function classification data analysis models. /// [StorableType("E7A8648D-C938-499F-A712-185542095708")] [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")] public class DiscriminantFunctionClassificationModel : ClassificationModel, IDiscriminantFunctionClassificationModel { public override IEnumerable VariablesUsedForPrediction { get { return model.VariablesUsedForPrediction; } } [Storable] private IRegressionModel model; public IRegressionModel Model { get { return model; } private set { model = value; } } [Storable] private double[] classValues; public IEnumerable ClassValues { get { return (double[])classValues.Clone(); } private set { classValues = value.ToArray(); } } [Storable] private double[] thresholds; public IEnumerable Thresholds { get { return (IEnumerable)thresholds.Clone(); } private set { thresholds = value.ToArray(); } } private IDiscriminantFunctionThresholdCalculator thresholdCalculator; [Storable] public IDiscriminantFunctionThresholdCalculator ThresholdCalculator { get { return thresholdCalculator; } private set { thresholdCalculator = value; } } [StorableConstructor] protected DiscriminantFunctionClassificationModel(StorableConstructorFlag _) : base(_) { } protected DiscriminantFunctionClassificationModel(DiscriminantFunctionClassificationModel original, Cloner cloner) : base(original, cloner) { model = cloner.Clone(original.model); classValues = (double[])original.classValues.Clone(); thresholds = (double[])original.thresholds.Clone(); } public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator) : base(model.TargetVariable) { this.name = ItemName; this.description = ItemDescription; this.model = model; this.classValues = new double[0]; this.thresholds = new double[0]; this.thresholdCalculator = thresholdCalculator; } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator(); } public override IDeepCloneable Clone(Cloner cloner) { return new DiscriminantFunctionClassificationModel(this, cloner); } public void SetThresholdsAndClassValues(IEnumerable thresholds, IEnumerable classValues) { var classValuesArr = classValues.ToArray(); var thresholdsArr = thresholds.ToArray(); if (thresholdsArr.Length != classValuesArr.Length) throw new ArgumentException(); this.classValues = classValuesArr; this.thresholds = thresholdsArr; OnThresholdsChanged(EventArgs.Empty); } public virtual void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable rows) { double[] classValues; double[] thresholds; var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, rows); thresholdCalculator.Calculate(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds); SetThresholdsAndClassValues(thresholds, classValues); } public IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { return model.GetEstimatedValues(dataset, rows); } public override IEnumerable GetEstimatedClassValues(IDataset dataset, IEnumerable rows) { var estimatedValues = GetEstimatedValues(dataset, rows); return GetEstimatedClassValues(estimatedValues); } public virtual IEnumerable GetEstimatedClassValues(IEnumerable estimatedValues) { if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current classification model."); foreach (var x in estimatedValues) { int classIndex = 0; // find first threshold value which is larger than x => class index = threshold index + 1 for (int i = 0; i < thresholds.Length; i++) { if (x > thresholds[i]) classIndex++; else break; } yield return classValues.ElementAt(classIndex - 1); } } #region events public event EventHandler ThresholdsChanged; protected virtual void OnThresholdsChanged(EventArgs e) { var listener = ThresholdsChanged; if (listener != null) listener(this, e); } #endregion public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) { return CreateDiscriminantFunctionClassificationSolution(problemData); } public virtual IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution( IClassificationProblemData problemData) { return new DiscriminantFunctionClassificationSolution(this, new ClassificationProblemData(problemData)); } } }