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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/AbsoluteErrorLoss.cs @ 12873

Last change on this file since 12873 was 12873, checked in by gkronber, 7 years ago

#2450 derived ILossFunction from IItem to allow execution on hive without privileged flag (made an "after deserialization"-hook necessary to convert the parameter type)

File size: 3.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 * and the BEACON Center for the Study of Evolution in Action.
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21#endregion
22
23using System;
24using System.Collections.Generic;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  // loss function for the weighted absolute error
31  [StorableClass]
32  [Item("Absolute error loss", "")]
33  public class AbsoluteErrorLoss : Item, ILossFunction {
34    public AbsoluteErrorLoss() { }
35
36    public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
37      var targetEnum = target.GetEnumerator();
38      var predEnum = pred.GetEnumerator();
39
40      double s = 0;
41      while (targetEnum.MoveNext() & predEnum.MoveNext()) {
42        double res = targetEnum.Current - predEnum.Current;
43        s += Math.Abs(res);  // |res|
44      }
45      if (targetEnum.MoveNext() | predEnum.MoveNext())
46        throw new ArgumentException("target and pred have differing lengths");
47
48      return s;
49    }
50
51    public IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred) {
52      var targetEnum = target.GetEnumerator();
53      var predEnum = pred.GetEnumerator();
54
55      while (targetEnum.MoveNext() & predEnum.MoveNext()) {
56        // dL(y, f(x)) / df(x) = sign(res)
57        var res = targetEnum.Current - predEnum.Current;
58        if (res > 0) yield return 1.0;
59        else if (res < 0) yield return -1.0;
60        else yield return 0.0;
61      }
62      if (targetEnum.MoveNext() | predEnum.MoveNext())
63        throw new ArgumentException("target and pred have differing lengths");
64    }
65
66    // return median of residuals
67    // targetArr and predArr are not changed by LineSearch
68    public double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx) {
69      if (targetArr.Length != predArr.Length)
70        throw new ArgumentException("target and pred have differing lengths");
71
72      // Median() is allocating an array anyway
73      // It would be possible to pre-allocated an array for the residuals if Median() would allow specification of a sub-range
74      int nRows = endIdx - startIdx + 1;
75      var res = new double[nRows];
76      for (int i = startIdx; i <= endIdx; i++) {
77        var row = idx[i];
78        res[i - startIdx] = targetArr[row] - predArr[row];
79      }
80      return res.Median(); // TODO: improve efficiency
81    }
82
83    #region item implementation
84    private AbsoluteErrorLoss(AbsoluteErrorLoss original, Cloner cloner) : base(original, cloner) { }
85
86    public override IDeepCloneable Clone(Cloner cloner) {
87      return new AbsoluteErrorLoss(this, cloner);
88    }
89    #endregion
90  }
91}
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