[5649] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[15973] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5649] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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[6233] | 22 | using System;
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[5649] | 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 |
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| 30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
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| 31 | /// <summary>
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| 32 | /// Represents a symbolic classification model
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| 33 | /// </summary>
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| 34 | [StorableClass]
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| 35 | [Item(Name = "SymbolicDiscriminantFunctionClassificationModel", Description = "Represents a symbolic classification model unsing a discriminant function.")]
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[8594] | 36 | public class SymbolicDiscriminantFunctionClassificationModel : SymbolicClassificationModel, ISymbolicDiscriminantFunctionClassificationModel {
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[5649] | 37 |
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| 38 | [Storable]
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| 39 | private double[] thresholds;
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| 40 | public IEnumerable<double> Thresholds {
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| 41 | get { return (IEnumerable<double>)thresholds.Clone(); }
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[5736] | 42 | private set { thresholds = value.ToArray(); }
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[5649] | 43 | }
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[5678] | 44 | [Storable]
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| 45 | private double[] classValues;
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| 46 | public IEnumerable<double> ClassValues {
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| 47 | get { return (IEnumerable<double>)classValues.Clone(); }
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[5736] | 48 | private set { classValues = value.ToArray(); }
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[5678] | 49 | }
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[8594] | 50 |
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| 51 | private IDiscriminantFunctionThresholdCalculator thresholdCalculator;
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[5720] | 52 | [Storable]
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[8594] | 53 | public IDiscriminantFunctionThresholdCalculator ThresholdCalculator {
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| 54 | get { return thresholdCalculator; }
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| 55 | private set { thresholdCalculator = value; }
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| 56 | }
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[5720] | 57 |
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[8594] | 58 |
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[5649] | 59 | [StorableConstructor]
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| 60 | protected SymbolicDiscriminantFunctionClassificationModel(bool deserializing) : base(deserializing) { }
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| 61 | protected SymbolicDiscriminantFunctionClassificationModel(SymbolicDiscriminantFunctionClassificationModel original, Cloner cloner)
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| 62 | : base(original, cloner) {
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| 63 | classValues = (double[])original.classValues.Clone();
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| 64 | thresholds = (double[])original.thresholds.Clone();
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[8594] | 65 | thresholdCalculator = cloner.Clone(original.thresholdCalculator);
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[5649] | 66 | }
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[15973] | 67 | public SymbolicDiscriminantFunctionClassificationModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDiscriminantFunctionThresholdCalculator thresholdCalculator,
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[5720] | 68 | double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
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[15973] | 69 | : base(targetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit) {
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[8531] | 70 | this.thresholds = new double[0];
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| 71 | this.classValues = new double[0];
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[8594] | 72 | this.ThresholdCalculator = thresholdCalculator;
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[5649] | 73 | }
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| 74 |
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[8594] | 75 | [StorableHook(HookType.AfterDeserialization)]
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| 76 | private void AfterDeserialization() {
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[8883] | 77 | // BackwardsCompatibility3.4
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| 78 | #region Backwards compatible code, remove with 3.5
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[8594] | 79 | if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
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[8883] | 80 | #endregion
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[8594] | 81 | }
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| 82 |
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[5649] | 83 | public override IDeepCloneable Clone(Cloner cloner) {
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| 84 | return new SymbolicDiscriminantFunctionClassificationModel(this, cloner);
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| 85 | }
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| 86 |
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[5736] | 87 | public void SetThresholdsAndClassValues(IEnumerable<double> thresholds, IEnumerable<double> classValues) {
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| 88 | var classValuesArr = classValues.ToArray();
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| 89 | var thresholdsArr = thresholds.ToArray();
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[12509] | 90 | if (thresholdsArr.Length != classValuesArr.Length || thresholdsArr.Length < 1)
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[8921] | 91 | throw new ArgumentException();
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[12509] | 92 | if (!double.IsNegativeInfinity(thresholds.First()))
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[8921] | 93 | throw new ArgumentException();
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[5736] | 94 |
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| 95 | this.classValues = classValuesArr;
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| 96 | this.thresholds = thresholdsArr;
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| 97 | OnThresholdsChanged(EventArgs.Empty);
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| 98 | }
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| 99 |
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[8594] | 100 | public override void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable<int> rows) {
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| 101 | double[] classValues;
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| 102 | double[] thresholds;
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| 103 | var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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| 104 | var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, rows);
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| 105 | thresholdCalculator.Calculate(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds);
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| 106 | SetThresholdsAndClassValues(thresholds, classValues);
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| 107 | }
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| 108 |
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[12509] | 109 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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[8594] | 110 | return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows).LimitToRange(LowerEstimationLimit, UpperEstimationLimit);
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[5649] | 111 | }
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| 112 |
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[12509] | 113 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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[8531] | 114 | if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current symbolic classification model.");
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[5649] | 115 | foreach (var x in GetEstimatedValues(dataset, rows)) {
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| 116 | int classIndex = 0;
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[5678] | 117 | // find first threshold value which is larger than x => class index = threshold index + 1
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[5649] | 118 | for (int i = 0; i < thresholds.Length; i++) {
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| 119 | if (x > thresholds[i]) classIndex++;
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| 120 | else break;
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| 121 | }
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[5657] | 122 | yield return classValues.ElementAt(classIndex - 1);
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[5649] | 123 | }
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| 124 | }
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| 125 |
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[8594] | 126 |
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| 127 | public override ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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| 128 | return CreateDiscriminantClassificationSolution(problemData);
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| 129 | }
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| 130 | public SymbolicDiscriminantFunctionClassificationSolution CreateDiscriminantClassificationSolution(IClassificationProblemData problemData) {
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[8528] | 131 | return new SymbolicDiscriminantFunctionClassificationSolution(this, new ClassificationProblemData(problemData));
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[6604] | 132 | }
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| 133 | IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
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[8594] | 134 | return CreateDiscriminantClassificationSolution(problemData);
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[6604] | 135 | }
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| 136 | IDiscriminantFunctionClassificationSolution IDiscriminantFunctionClassificationModel.CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData) {
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[8594] | 137 | return CreateDiscriminantClassificationSolution(problemData);
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[6604] | 138 | }
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| 139 |
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[5649] | 140 | #region events
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| 141 | public event EventHandler ThresholdsChanged;
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| 142 | protected virtual void OnThresholdsChanged(EventArgs e) {
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| 143 | var listener = ThresholdsChanged;
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| 144 | if (listener != null) listener(this, e);
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| 145 | }
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[5720] | 146 | #endregion
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[5649] | 147 | }
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| 148 | }
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