source: branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.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: 5.6 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.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28using HeuristicLab.Problems.DataAnalysis;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  /// <summary>
32  /// Represents a multinomial logit model for classification
33  /// </summary>
34  [StorableClass]
35  [Item("Multinomial Logit Model", "Represents a multinomial logit model for classification.")]
36  public sealed class MultinomialLogitModel : ClassificationModel {
37
38    private alglib.logitmodel logitModel;
39    public alglib.logitmodel Model {
40      get { return logitModel; }
41      set {
42        if (value != logitModel) {
43          if (value == null) throw new ArgumentNullException();
44          logitModel = value;
45          OnChanged(EventArgs.Empty);
46        }
47      }
48    }
49
50    public override IEnumerable<string> VariablesUsedForPrediction {
51      get { return allowedInputVariables; }
52    }
53
54    [Storable]
55    private string[] allowedInputVariables;
56    [Storable]
57    private double[] classValues;
58    [Storable]
59    private List<KeyValuePair<string, IEnumerable<string>>> factorVariables;
60
61    [StorableConstructor]
62    private MultinomialLogitModel(bool deserializing)
63      : base(deserializing) {
64      if (deserializing)
65        logitModel = new alglib.logitmodel();
66    }
67    private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner)
68      : base(original, cloner) {
69      logitModel = new alglib.logitmodel();
70      logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
71      allowedInputVariables = (string[])original.allowedInputVariables.Clone();
72      classValues = (double[])original.classValues.Clone();
73      this.factorVariables = original.factorVariables.Select(kvp => new KeyValuePair<string, IEnumerable<string>>(kvp.Key, new List<string>(kvp.Value))).ToList();
74    }
75    public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> doubleInputVariables, IEnumerable<KeyValuePair<string, IEnumerable<string>>> factorVariables, double[] classValues)
76      : base(targetVariable) {
77      this.name = ItemName;
78      this.description = ItemDescription;
79      this.logitModel = logitModel;
80      this.allowedInputVariables = doubleInputVariables.ToArray();
81      this.factorVariables = factorVariables.Select(kvp => new KeyValuePair<string, IEnumerable<string>>(kvp.Key, new List<string>(kvp.Value))).ToList();
82      this.classValues = (double[])classValues.Clone();
83    }
84
85    [StorableHook(HookType.AfterDeserialization)]
86    private void AfterDeserialization() {
87      // BackwardsCompatibility3.3
88      #region Backwards compatible code, remove with 3.4
89      factorVariables = new List<KeyValuePair<string, IEnumerable<string>>>();
90      #endregion
91    }
92
93    public override IDeepCloneable Clone(Cloner cloner) {
94      return new MultinomialLogitModel(this, cloner);
95    }
96
97    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
98
99      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
100      double[,] factorData = AlglibUtil.PrepareInputMatrix(dataset, factorVariables, rows);
101
102      inputData = factorData.HorzCat(inputData);
103
104      int n = inputData.GetLength(0);
105      int columns = inputData.GetLength(1);
106      double[] x = new double[columns];
107      double[] y = new double[classValues.Length];
108
109      for (int row = 0; row < n; row++) {
110        for (int column = 0; column < columns; column++) {
111          x[column] = inputData[row, column];
112        }
113        alglib.mnlprocess(logitModel, x, ref y);
114        // find class for with the largest probability value
115        int maxProbClassIndex = 0;
116        double maxProb = y[0];
117        for (int i = 1; i < y.Length; i++) {
118          if (maxProb < y[i]) {
119            maxProb = y[i];
120            maxProbClassIndex = i;
121          }
122        }
123        yield return classValues[maxProbClassIndex];
124      }
125    }
126
127    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
128      return new MultinomialLogitClassificationSolution(this, new ClassificationProblemData(problemData));
129    }
130
131    #region events
132    public event EventHandler Changed;
133    private void OnChanged(EventArgs e) {
134      var handlers = Changed;
135      if (handlers != null)
136        handlers(this, e);
137    }
138    #endregion
139
140    #region persistence
141    [Storable]
142    private double[] LogitModelW {
143      get {
144        return logitModel.innerobj.w;
145      }
146      set {
147        logitModel.innerobj.w = value;
148      }
149    }
150    #endregion
151
152  }
153}
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