source: branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourClassification.cs @ 14836

Last change on this file since 14836 was 14836, checked in by gkronber, 2 years ago

#2700 merged changesets from trunk to branch

File size: 5.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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.Linq;
24using System.Threading;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Optimization;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31using HeuristicLab.Problems.DataAnalysis;
32
33namespace HeuristicLab.Algorithms.DataAnalysis {
34  /// <summary>
35  /// Nearest neighbour classification data analysis algorithm.
36  /// </summary>
37  [Item("Nearest Neighbour Classification (kNN)", "Nearest neighbour classification data analysis algorithm (wrapper for ALGLIB).")]
38  [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 150)]
39  [StorableClass]
40  public sealed class NearestNeighbourClassification : FixedDataAnalysisAlgorithm<IClassificationProblem> {
41    private const string KParameterName = "K";
42    private const string NearestNeighbourClassificationModelResultName = "Nearest neighbour classification solution";
43    private const string WeightsParameterName = "Weights";
44
45
46    #region parameter properties
47    public IFixedValueParameter<IntValue> KParameter {
48      get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
49    }
50    public IValueParameter<DoubleArray> WeightsParameter {
51      get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }
52    }
53    #endregion
54    #region properties
55    public int K {
56      get { return KParameter.Value.Value; }
57      set {
58        if (value <= 0) throw new ArgumentException("K must be larger than zero.", "K");
59        else KParameter.Value.Value = value;
60      }
61    }
62    public DoubleArray Weights {
63      get { return WeightsParameter.Value; }
64      set { WeightsParameter.Value = value; }
65    }
66    #endregion
67
68    [StorableConstructor]
69    private NearestNeighbourClassification(bool deserializing) : base(deserializing) { }
70    private NearestNeighbourClassification(NearestNeighbourClassification original, Cloner cloner)
71      : base(original, cloner) {
72    }
73    public NearestNeighbourClassification()
74      : base() {
75      Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
76      Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));
77      Problem = new ClassificationProblem();
78    }
79    [StorableHook(HookType.AfterDeserialization)]
80    private void AfterDeserialization() {
81      // BackwardsCompatibility3.3
82      #region Backwards compatible code, remove with 3.4
83      if (!Parameters.ContainsKey(WeightsParameterName)) {
84        Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));
85      }
86      #endregion
87    }
88
89    public override IDeepCloneable Clone(Cloner cloner) {
90      return new NearestNeighbourClassification(this, cloner);
91    }
92
93    #region nearest neighbour
94    protected override void Run(CancellationToken cancellationToken) {
95      double[] weights = null;
96      if (Weights != null) weights = Weights.CloneAsArray();
97      var solution = CreateNearestNeighbourClassificationSolution(Problem.ProblemData, K, weights);
98      Results.Add(new Result(NearestNeighbourClassificationModelResultName, "The nearest neighbour classification solution.", solution));
99    }
100
101    public static IClassificationSolution CreateNearestNeighbourClassificationSolution(IClassificationProblemData problemData, int k, double[] weights = null) {
102      var problemDataClone = (IClassificationProblemData)problemData.Clone();
103      return new NearestNeighbourClassificationSolution(Train(problemDataClone, k, weights), problemDataClone);
104    }
105
106    public static INearestNeighbourModel Train(IClassificationProblemData problemData, int k, double[] weights = null) {
107      return new NearestNeighbourModel(problemData.Dataset,
108        problemData.TrainingIndices,
109        k,
110        problemData.TargetVariable,
111        problemData.AllowedInputVariables,
112        weights,
113        problemData.ClassValues.ToArray());
114    }
115    #endregion
116  }
117}
Note: See TracBrowser for help on using the repository browser.