[8606] | 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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[8606] | 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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| 22 | using System;
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| 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 nearest neighbour model for regression and classification
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| 33 | /// </summary>
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| 34 | [StorableClass]
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| 35 | [Item("SymbolicNearestNeighbourClassificationModel", "Represents a nearest neighbour model for symbolic classification.")]
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| 36 | public sealed class SymbolicNearestNeighbourClassificationModel : SymbolicClassificationModel {
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| 37 |
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| 38 | [Storable]
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| 39 | private int k;
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| 40 | [Storable]
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[9003] | 41 | private List<double> trainedClasses;
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[8978] | 42 | [Storable]
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[9003] | 43 | private List<double> trainedEstimatedValues;
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| 44 |
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| 45 | [Storable]
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[8978] | 46 | private ClassFrequencyComparer frequencyComparer;
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[8606] | 47 |
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| 48 | [StorableConstructor]
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| 49 | private SymbolicNearestNeighbourClassificationModel(bool deserializing) : base(deserializing) { }
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| 50 | private SymbolicNearestNeighbourClassificationModel(SymbolicNearestNeighbourClassificationModel original, Cloner cloner)
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| 51 | : base(original, cloner) {
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| 52 | k = original.k;
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[8978] | 53 | frequencyComparer = new ClassFrequencyComparer(original.frequencyComparer);
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[9003] | 54 | trainedEstimatedValues = new List<double>(original.trainedEstimatedValues);
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| 55 | trainedClasses = new List<double>(original.trainedClasses);
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[8606] | 56 | }
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[15973] | 57 | public SymbolicNearestNeighbourClassificationModel(string targetVariable, int k, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
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| 58 | : base(targetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit) {
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[8606] | 59 | this.k = k;
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[8978] | 60 | frequencyComparer = new ClassFrequencyComparer();
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[9003] | 61 |
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[8606] | 62 | }
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| 63 |
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| 64 | public override IDeepCloneable Clone(Cloner cloner) {
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| 65 | return new SymbolicNearestNeighbourClassificationModel(this, cloner);
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| 66 | }
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| 67 |
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[12509] | 68 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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[8978] | 69 | var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows)
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| 70 | .LimitToRange(LowerEstimationLimit, UpperEstimationLimit);
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[8606] | 71 | foreach (var ev in estimatedValues) {
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[9003] | 72 | // find the range [lower, upper[ of trainedTargetValues that contains the k closest neighbours
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| 73 | // the range can span more than k elements when there are equal estimated values
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| 74 |
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[8978] | 75 | // find the index of the training-point to which distance is shortest
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[9003] | 76 | int lower = trainedEstimatedValues.BinarySearch(ev);
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| 77 | int upper;
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| 78 | // if the element was not found exactly, BinarySearch returns the complement of the index of the next larger item
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| 79 | if (lower < 0) {
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| 80 | lower = ~lower;
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| 81 | // lower is not necessarily the closer one
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| 82 | // determine which element is closer to ev (lower - 1) or (lower)
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| 83 | if (lower == trainedEstimatedValues.Count ||
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| 84 | (lower > 0 && Math.Abs(ev - trainedEstimatedValues[lower - 1]) < Math.Abs(ev - trainedEstimatedValues[lower]))) {
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| 85 | lower = lower - 1;
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| 86 | }
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| 87 | }
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| 88 | upper = lower + 1;
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| 89 | // at this point we have a range [lower, upper[ that includes only the closest element to ev
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| 90 |
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| 91 | // expand the range to left or right looking for the nearest neighbors
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| 92 | while (upper - lower < Math.Min(k, trainedEstimatedValues.Count)) {
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| 93 | bool lowerIsCloser = upper >= trainedEstimatedValues.Count ||
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| 94 | (lower > 0 && ev - trainedEstimatedValues[lower] <= trainedEstimatedValues[upper] - ev);
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| 95 | bool upperIsCloser = lower <= 0 ||
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| 96 | (upper < trainedEstimatedValues.Count &&
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| 97 | ev - trainedEstimatedValues[lower] >= trainedEstimatedValues[upper] - ev);
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| 98 | if (!lowerIsCloser && !upperIsCloser) break;
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[9002] | 99 | if (lowerIsCloser) {
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[8606] | 100 | lower--;
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[9003] | 101 | // eat up all equal values
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| 102 | while (lower > 0 && trainedEstimatedValues[lower - 1].IsAlmost(trainedEstimatedValues[lower]))
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| 103 | lower--;
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[9002] | 104 | }
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| 105 | if (upperIsCloser) {
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[8606] | 106 | upper++;
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[9003] | 107 | while (upper < trainedEstimatedValues.Count &&
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| 108 | trainedEstimatedValues[upper - 1].IsAlmost(trainedEstimatedValues[upper]))
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| 109 | upper++;
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[8606] | 110 | }
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| 111 | }
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[8978] | 112 | // majority voting with preference for bigger class in case of tie
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[9003] | 113 | yield return Enumerable.Range(lower, upper - lower)
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| 114 | .Select(i => trainedClasses[i])
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| 115 | .GroupBy(c => c)
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| 116 | .Select(g => new { Class = g.Key, Votes = g.Count() })
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| 117 | .MaxItems(p => p.Votes)
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| 118 | .OrderByDescending(m => m.Class, frequencyComparer)
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| 119 | .First().Class;
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[8606] | 120 | }
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| 121 | }
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| 122 |
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| 123 | public override void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable<int> rows) {
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[8978] | 124 | var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, problemData.Dataset, rows)
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| 125 | .LimitToRange(LowerEstimationLimit, UpperEstimationLimit);
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[8606] | 126 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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[9003] | 127 | var trainedClasses = targetValues.ToArray();
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| 128 | var trainedEstimatedValues = estimatedValues.ToArray();
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[8606] | 129 |
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[9003] | 130 | Array.Sort(trainedEstimatedValues, trainedClasses);
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| 131 | this.trainedClasses = new List<double>(trainedClasses);
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| 132 | this.trainedEstimatedValues = new List<double>(trainedEstimatedValues);
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[8606] | 133 |
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[9003] | 134 | var freq = trainedClasses
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| 135 | .GroupBy(c => c)
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| 136 | .ToDictionary(g => g.Key, g => g.Count());
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| 137 | this.frequencyComparer = new ClassFrequencyComparer(freq);
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[8606] | 138 | }
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| 139 |
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| 140 | public override ISymbolicClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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[9002] | 141 | return new SymbolicClassificationSolution((ISymbolicClassificationModel)Clone(), problemData);
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[8606] | 142 | }
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| 143 | }
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[8978] | 144 |
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| 145 | [StorableClass]
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[8979] | 146 | internal sealed class ClassFrequencyComparer : IComparer<double> {
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[8978] | 147 | [Storable]
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[9002] | 148 | private readonly Dictionary<double, int> classFrequencies;
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[8978] | 149 |
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| 150 | [StorableConstructor]
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| 151 | private ClassFrequencyComparer(bool deserializing) { }
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| 152 | public ClassFrequencyComparer() {
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| 153 | classFrequencies = new Dictionary<double, int>();
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| 154 | }
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| 155 | public ClassFrequencyComparer(Dictionary<double, int> frequencies) {
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| 156 | classFrequencies = frequencies;
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| 157 | }
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| 158 | public ClassFrequencyComparer(ClassFrequencyComparer original) {
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| 159 | classFrequencies = new Dictionary<double, int>(original.classFrequencies);
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| 160 | }
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| 161 |
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| 162 | public int Compare(double x, double y) {
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| 163 | bool cx = classFrequencies.ContainsKey(x), cy = classFrequencies.ContainsKey(y);
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| 164 | if (cx && cy)
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| 165 | return classFrequencies[x].CompareTo(classFrequencies[y]);
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| 166 | if (cx) return 1;
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| 167 | return -1;
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| 168 | }
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| 169 | }
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[8606] | 170 | }
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