source: branches/2994-AutoDiffForIntervals/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/QuantileRegressionLoss.cs @ 17209

Last change on this file since 17209 was 17209, checked in by gkronber, 2 months ago

#2994: merged r17132:17198 from trunk to branch

File size: 4.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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.Data;
28using HeuristicLab.Parameters;
29using HEAL.Attic;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  // loss function for quantile regression
33  // Generalized Boosted Models - A Guide To The gbm Package, Greg Ridgeway, August 2007, page 11
34  [StorableType("B8EF6C18-C1A8-4B43-8FEA-A6900287ADDD")]
35  [Item("QuantileRegressionloss", "Loss function for quantile regression")]
36  public sealed class QuantileRegressionLoss : ParameterizedNamedItem, ILossFunction {
37    public IFixedValueParameter<PercentValue> AlphaParameter {
38      get { return (IFixedValueParameter<PercentValue>)Parameters["Alpha"]; }
39    }
40
41    public double Alpha {
42      get { return AlphaParameter.Value.Value; }
43      set {
44        if (value <= 0.0 || value >= 1.0) throw new ArgumentException("Valid values for alpha: 0 < alpha < 1");
45        AlphaParameter.Value.Value = value;
46      }
47    }
48
49    public QuantileRegressionLoss()
50      : base("QuantileRegressionLoss", "Loss function for quantile regression") {
51      Parameters.Add(new FixedValueParameter<PercentValue>("Alpha", new PercentValue(0.9)));
52    }
53
54    public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
55      var targetEnum = target.GetEnumerator();
56      var predEnum = pred.GetEnumerator();
57      var alpha = Alpha;
58      double leftSum = 0;
59      double rightsum = 0;
60      while (targetEnum.MoveNext() & predEnum.MoveNext()) {
61        double res = targetEnum.Current - predEnum.Current;
62        if (res > 0) leftSum += res;
63        else rightsum += -res;
64      }
65      if (targetEnum.MoveNext() | predEnum.MoveNext())
66        throw new ArgumentException("target and pred have differing lengths");
67
68      return alpha * leftSum + (1 - alpha) * rightsum;
69    }
70
71    public IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred) {
72      var targetEnum = target.GetEnumerator();
73      var predEnum = pred.GetEnumerator();
74      var alpha = AlphaParameter.Value.Value;
75
76      while (targetEnum.MoveNext() & predEnum.MoveNext()) {
77        var res = targetEnum.Current - predEnum.Current;
78        if (res > 0) yield return alpha;
79        else if (res < 0) yield return -(1.0 - alpha);
80        else yield return 0.0;
81      }
82      if (targetEnum.MoveNext() | predEnum.MoveNext())
83        throw new ArgumentException("target and pred have differing lengths");
84    }
85
86    // targetArr and predArr are not changed by LineSearch
87    public double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx) {
88      if (targetArr.Length != predArr.Length)
89        throw new ArgumentException("target and pred have differing lengths");
90
91      // Quantile() is allocating an array anyway
92      // It would be possible to pre-allocated an array for the residuals if Quantile() would allow specification of a sub-range
93      int nRows = endIdx - startIdx + 1;
94      var res = new double[nRows];
95      for (int i = startIdx; i <= endIdx; i++) {
96        var row = idx[i];
97        res[i - startIdx] = targetArr[row] - predArr[row];
98      }
99      return res.Quantile(Alpha);
100    }
101
102    #region item implementation
103    [StorableConstructor]
104    private QuantileRegressionLoss(StorableConstructorFlag _) : base(_) { }
105
106    private QuantileRegressionLoss(QuantileRegressionLoss original, Cloner cloner)
107      : base(original, cloner) {
108    }
109
110    public override IDeepCloneable Clone(Cloner cloner) {
111      return new QuantileRegressionLoss(this, cloner);
112    }
113    #endregion
114
115    public override bool CanChangeName { get { return false; } }
116    public override bool CanChangeDescription { get { return false; } }
117  }
118}
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