1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Problems.DataAnalysis;
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27 | using HeuristicLab.Random;
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28 |
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29 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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30 | public class FeatureSelectionInstanceProvider : ArtificialRegressionInstanceProvider {
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31 | public override string Name {
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32 | get { return "Feature Selection Problems"; }
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33 | }
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34 | public override string Description {
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35 | get { return "A set of artificial feature selection benchmark problems"; }
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36 | }
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37 | public override Uri WebLink {
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38 | get { return new Uri("http://dev.heuristiclab.com"); }
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39 | }
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40 | public override string ReferencePublication {
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41 | get { return ""; }
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42 | }
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43 |
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44 | public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
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45 | var sizes = new int[] { 50, 100, 200 };
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46 | var pp = new double[] { 0.1, 0.25, 0.5 };
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47 | var noiseRatios = new double[] { 0.01, 0.05, 0.1, 0.2 };
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48 | var mt = new MersenneTwister();
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49 | var xGenerator = new NormalDistributedRandom(mt, 0, 1);
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50 | var weightGenerator = new UniformDistributedRandom(mt, 0, 10);
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51 | return (from size in sizes
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52 | from p in pp
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53 | from noiseRatio in noiseRatios
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54 | select new FeatureSelection(size, p, noiseRatio, xGenerator, weightGenerator))
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55 | .Cast<IDataDescriptor>()
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56 | .ToList();
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57 | }
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58 |
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59 | public override IRegressionProblemData LoadData(IDataDescriptor descriptor) {
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60 | var featureSelectionDescriptor = descriptor as FeatureSelection;
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61 | if (featureSelectionDescriptor == null) throw new ArgumentException("FeatureSelectionInstanceProvider expects an FeatureSelection data descriptor.");
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62 | // base call generates a regression problem data
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63 | var regProblemData = base.LoadData(featureSelectionDescriptor);
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64 | var problemData =
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65 | new FeatureSelectionRegressionProblemData(
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66 | regProblemData.Dataset, regProblemData.AllowedInputVariables, regProblemData.TargetVariable,
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67 | featureSelectionDescriptor.SelectedFeatures, featureSelectionDescriptor.Weights,
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68 | featureSelectionDescriptor.OptimalRSquared);
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69 |
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70 | // copy values from regProblemData to feature selection problem data
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71 | problemData.Name = regProblemData.Name;
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72 | problemData.Description = regProblemData.Description;
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73 | problemData.TrainingPartition.Start = regProblemData.TrainingPartition.Start;
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74 | problemData.TrainingPartition.End = regProblemData.TrainingPartition.End;
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75 | problemData.TestPartition.Start = regProblemData.TestPartition.Start;
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76 | problemData.TestPartition.End = regProblemData.TestPartition.End;
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77 |
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78 | return problemData;
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79 | }
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80 | }
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81 | }
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