[13026] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | * and the BEACON Center for the Study of Evolution in Action.
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| 5 | *
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| 6 | * This file is part of HeuristicLab.
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| 7 | *
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| 8 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 9 | * it under the terms of the GNU General Public License as published by
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| 10 | * the Free Software Foundation, either version 3 of the License, or
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| 11 | * (at your option) any later version.
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| 12 | *
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| 13 | * HeuristicLab is distributed in the hope that it will be useful,
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| 14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 16 | * GNU General Public License for more details.
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| 17 | *
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| 18 | * You should have received a copy of the GNU General Public License
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| 19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 20 | */
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| 21 | #endregion
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| 22 |
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| 23 | using System;
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| 24 | using System.Collections.Generic;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Parameters;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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| 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 32 | // loss function for quantile regression
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| 33 | // Generalized Boosted Models - A Guide To The gbm Package, Greg Ridgeway, August 2007, page 11
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| 34 | [StorableClass]
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| 35 | [Item("QuantileRegressionloss", "Loss function for quantile regression")]
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| 36 | public sealed class QuantileRegressionLoss : ParameterizedNamedItem, ILossFunction {
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| 37 | public IFixedValueParameter<PercentValue> AlphaParameter {
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| 38 | get { return (IFixedValueParameter<PercentValue>)Parameters["Alpha"]; }
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| 39 | }
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| 40 |
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| 41 | public double Alpha {
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| 42 | get { return AlphaParameter.Value.Value; }
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| 43 | set {
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| 44 | if (value <= 0.0 || value >= 1.0) throw new ArgumentException("Valid values for alpha: 0 < alpha < 1");
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| 45 | AlphaParameter.Value.Value = value;
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| 46 | }
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| 47 | }
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| 48 |
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| 49 | public QuantileRegressionLoss()
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| 50 | : base("QuantileRegressionLoss", "Loss function for quantile regression") {
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| 51 | Parameters.Add(new FixedValueParameter<PercentValue>("Alpha", new PercentValue(0.9)));
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| 52 | }
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| 53 |
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| 54 | public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
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| 55 | var targetEnum = target.GetEnumerator();
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| 56 | var predEnum = pred.GetEnumerator();
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| 57 | var alpha = Alpha;
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| 58 | double leftSum = 0;
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| 59 | double rightsum = 0;
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| 60 | while (targetEnum.MoveNext() & predEnum.MoveNext()) {
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| 61 | double res = targetEnum.Current - predEnum.Current;
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| 62 | if (res > 0) leftSum += res;
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| 63 | else rightsum += -res;
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| 64 | }
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| 65 | if (targetEnum.MoveNext() | predEnum.MoveNext())
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| 66 | throw new ArgumentException("target and pred have differing lengths");
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| 67 |
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| 68 | return alpha * leftSum + (1 - alpha) * rightsum;
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| 69 | }
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| 70 |
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| 71 | public IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred) {
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| 72 | var targetEnum = target.GetEnumerator();
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| 73 | var predEnum = pred.GetEnumerator();
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| 74 | var alpha = AlphaParameter.Value.Value;
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| 75 |
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| 76 | while (targetEnum.MoveNext() & predEnum.MoveNext()) {
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| 77 | var res = targetEnum.Current - predEnum.Current;
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| 78 | if (res > 0) yield return alpha;
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| 79 | else if (res < 0) yield return -(1.0 - alpha);
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| 80 | else yield return 0.0;
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| 81 | }
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| 82 | if (targetEnum.MoveNext() | predEnum.MoveNext())
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| 83 | throw new ArgumentException("target and pred have differing lengths");
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| 84 | }
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| 85 |
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| 86 | // targetArr and predArr are not changed by LineSearch
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| 87 | public double LineSearch(double[] targetArr, double[] predArr, int[] idx, int startIdx, int endIdx) {
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| 88 | if (targetArr.Length != predArr.Length)
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| 89 | throw new ArgumentException("target and pred have differing lengths");
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| 90 |
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| 91 | // Quantile() is allocating an array anyway
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| 92 | // It would be possible to pre-allocated an array for the residuals if Quantile() would allow specification of a sub-range
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| 93 | int nRows = endIdx - startIdx + 1;
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| 94 | var res = new double[nRows];
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| 95 | for (int i = startIdx; i <= endIdx; i++) {
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| 96 | var row = idx[i];
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| 97 | res[i - startIdx] = targetArr[row] - predArr[row];
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| 98 | }
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| 99 | return res.Quantile(Alpha);
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| 100 | }
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| 101 |
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| 102 | #region item implementation
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| 103 | [StorableConstructor]
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| 104 | private QuantileRegressionLoss(bool deserializing) : base(deserializing) { }
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| 105 |
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| 106 | private QuantileRegressionLoss(QuantileRegressionLoss original, Cloner cloner)
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| 107 | : base(original, cloner) {
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| 108 | }
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| 109 |
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| 110 | public override IDeepCloneable Clone(Cloner cloner) {
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| 111 | return new QuantileRegressionLoss(this, cloner);
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| 112 | }
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| 113 | #endregion
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| 114 |
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| 115 | public override bool CanChangeName { get { return false; } }
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| 116 | public override bool CanChangeDescription { get { return false; } }
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| 117 | }
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| 118 | }
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