1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022015 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 System.Diagnostics;


26  using System.Linq;


27  using HeuristicLab.Common;


28 


29  namespace HeuristicLab.Algorithms.DataAnalysis {


30  // loss function for the weighted absolute error


31  public class AbsoluteErrorLoss : ILossFunction {


32  public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred, IEnumerable<double> weight) {


33  var targetEnum = target.GetEnumerator();


34  var predEnum = pred.GetEnumerator();


35  var weightEnum = weight.GetEnumerator();


36 


37  double s = 0;


38  while (targetEnum.MoveNext() & predEnum.MoveNext() & weightEnum.MoveNext()) {


39  double res = targetEnum.Current  predEnum.Current;


40  s += weightEnum.Current * Math.Abs(res);


41  }


42  if (targetEnum.MoveNext()  predEnum.MoveNext()  weightEnum.MoveNext())


43  throw new ArgumentException("target, pred and weight have differing lengths");


44 


45  return s;


46  }


47 


48  public IEnumerable<double> GetLossGradient(IEnumerable<double> target, IEnumerable<double> pred, IEnumerable<double> weight) {


49  var targetEnum = target.GetEnumerator();


50  var predEnum = pred.GetEnumerator();


51  var weightEnum = weight.GetEnumerator();


52 


53  while (targetEnum.MoveNext() & predEnum.MoveNext() & weightEnum.MoveNext()) {


54  var res = targetEnum.Current  predEnum.Current;


55  if (res > 0) yield return weightEnum.Current;


56  else if (res < 0) yield return weightEnum.Current;


57  else yield return 0.0;


58  }


59  if (targetEnum.MoveNext()  predEnum.MoveNext()  weightEnum.MoveNext())


60  throw new ArgumentException("target, pred and weight have differing lengths");


61  }


62 


63  public LineSearchFunc GetLineSearchFunc(IEnumerable<double> target, IEnumerable<double> pred, IEnumerable<double> weight) {


64  var targetArr = target.ToArray();


65  var predArr = pred.ToArray();


66  var weightArr = weight.ToArray();


67  // weights are not supported yet


68  // when weights are supported we need to calculate a weighted median


69  Debug.Assert(weightArr.All(w => w.IsAlmost(1.0)));


70 


71  if (targetArr.Length != predArr.Length  predArr.Length != weightArr.Length)


72  throw new ArgumentException("target, pred and weight have differing lengths");


73 


74  // line search for abs error


75  LineSearchFunc lineSearch = (idx, startIdx, endIdx) => {


76  // Median() is allocating an array anyway


77  // It would be possible to preallocated an array for the residuals if Median() would allow specification of a subrange


78  int nRows = endIdx  startIdx + 1;


79  var res = new double[nRows];


80  for (int offset = 0; offset < nRows; offset++) {


81  var i = startIdx + offset;


82  var row = idx[i];


83  res[offset] = targetArr[row]  predArr[row];


84  }


85  return res.Median();


86  };


87  return lineSearch;


88 


89  }


90 


91  public override string ToString() {


92  return "Absolute error loss";


93  }


94  }


95  }

