1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022016 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 


23  using System;


24  using System.Collections.Generic;


25  using HeuristicLab.Common;


26  using HeuristicLab.Core;


27  using HeuristicLab.Data;


28  using HeuristicLab.Parameters;


29  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


30 


31  namespace 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  [StorableClass]


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 preallocated an array for the residuals if Quantile() would allow specification of a subrange


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(bool deserializing) : base(deserializing) { }


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  }

