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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs @ 8139

Last change on this file since 8139 was 8139, checked in by mkommend, 12 years ago

#1722: Renamed indizes to indices in the whole trunk solution.

File size: 5.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Optimization;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31using HeuristicLab.Problems.DataAnalysis;
32using HeuristicLab.Problems.DataAnalysis.Symbolic;
33using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
34using HeuristicLab.Parameters;
35
36namespace HeuristicLab.Algorithms.DataAnalysis {
37  /// <summary>
38  /// Nearest neighbour classification data analysis algorithm.
39  /// </summary>
40  [Item("Nearest Neighbour Classification", "Nearest neighbour classification data analysis algorithm (wrapper for ALGLIB).")]
41  [Creatable("Data Analysis")]
42  [StorableClass]
43  public sealed class NearestNeighbourClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
44    private const string KParameterName = "K";
45    private const string NearestNeighbourClassificationModelResultName = "Nearest neighbour classification solution";
46
47    #region parameter properties
48    public IFixedValueParameter<IntValue> KParameter {
49      get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
50    }
51    #endregion
52    #region properties
53    public int K {
54      get { return KParameter.Value.Value; }
55      set {
56        if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");
57        else KParameter.Value.Value = value;
58      }
59    }
60    #endregion
61
62    [StorableConstructor]
63    private NearestNeighbourClassification(bool deserializing) : base(deserializing) { }
64    private NearestNeighbourClassification(NearestNeighbourClassification original, Cloner cloner)
65      : base(original, cloner) {
66    }
67    public NearestNeighbourClassification()
68      : base() {
69      Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
70      Problem = new ClassificationProblem();
71    }
72    [StorableHook(HookType.AfterDeserialization)]
73    private void AfterDeserialization() { }
74
75    public override IDeepCloneable Clone(Cloner cloner) {
76      return new NearestNeighbourClassification(this, cloner);
77    }
78
79    #region nearest neighbour
80    protected override void Run() {
81      var solution = CreateNearestNeighbourClassificationSolution(Problem.ProblemData, K);
82      Results.Add(new Result(NearestNeighbourClassificationModelResultName, "The nearest neighbour classification solution.", solution));
83    }
84
85    public static IClassificationSolution CreateNearestNeighbourClassificationSolution(IClassificationProblemData problemData, int k) {
86      Dataset dataset = problemData.Dataset;
87      string targetVariable = problemData.TargetVariable;
88      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
89      IEnumerable<int> rows = problemData.TrainingIndices;
90      double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
91      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
92        throw new NotSupportedException("Nearest neighbour classification does not support NaN or infinity values in the input dataset.");
93
94      alglib.nearestneighbor.kdtree kdtree = new alglib.nearestneighbor.kdtree();
95
96      int nRows = inputMatrix.GetLength(0);
97      int nFeatures = inputMatrix.GetLength(1) - 1;
98      double[] classValues = dataset.GetDoubleValues(targetVariable).Distinct().OrderBy(x => x).ToArray();
99      int nClasses = classValues.Count();
100      // map original class values to values [0..nClasses-1]
101      Dictionary<double, double> classIndices = new Dictionary<double, double>();
102      for (int i = 0; i < nClasses; i++) {
103        classIndices[classValues[i]] = i;
104      }
105      for (int row = 0; row < nRows; row++) {
106        inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
107      }
108      alglib.nearestneighbor.kdtreebuild(inputMatrix, nRows, inputMatrix.GetLength(1) - 1, 1, 2, kdtree);
109      var problemDataClone = (IClassificationProblemData) problemData.Clone();
110      return new NearestNeighbourClassificationSolution(problemDataClone, new NearestNeighbourModel(kdtree, k, targetVariable, allowedInputVariables, problemDataClone.ClassValues.ToArray()));
111    }
112    #endregion
113  }
114}
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