1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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 HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Parameters;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 | using HeuristicLab.Problems.DataAnalysis;
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30 |
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31 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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32 | [StorableClass]
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33 | public class FeatureSelectionRegressionProblemData : RegressionProblemData {
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34 | private const string SelectedFeaturesParameterName = "SelectedFeatures";
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35 | private const string WeightsParameterName = "Weights";
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36 | private const string OptimalRSquaredParameterName = "R² (best solution)";
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37 |
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38 | public IValueParameter<StringArray> SelectedFeaturesParameter {
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39 | get { return (IValueParameter<StringArray>)Parameters[SelectedFeaturesParameterName]; }
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40 | }
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41 |
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42 | public IValueParameter<DoubleArray> WeightsParameter {
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43 | get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }
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44 | }
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45 |
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46 | public IValueParameter<DoubleValue> OptimalRSquaredParameter {
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47 | get { return (IValueParameter<DoubleValue>)Parameters[OptimalRSquaredParameterName]; }
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48 | }
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49 |
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50 | [StorableConstructor]
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51 | protected FeatureSelectionRegressionProblemData(bool deserializing)
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52 | : base(deserializing) {
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53 | }
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54 | protected FeatureSelectionRegressionProblemData(FeatureSelectionRegressionProblemData original, Cloner cloner)
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55 | : base(original, cloner) {
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56 | }
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57 |
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58 | public FeatureSelectionRegressionProblemData(IDataset ds, IEnumerable<string> allowedInputVariables, string targetVariable, string[] selectedFeatures, double[] weights, double optimalRSquared)
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59 | : base(ds, allowedInputVariables, targetVariable) {
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60 | if (selectedFeatures.Length != weights.Length) throw new ArgumentException("Length of selected features vector does not match the length of the weights vector");
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61 | if (optimalRSquared < 0 || optimalRSquared > 1) throw new ArgumentException("Optimal R² is not in range [0..1]");
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62 | Parameters.Add(new FixedValueParameter<StringArray>(
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63 | SelectedFeaturesParameterName,
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64 | "Array of features used to generate the target values.",
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65 | new StringArray(selectedFeatures).AsReadOnly()));
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66 | Parameters.Add(new FixedValueParameter<DoubleArray>(
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67 | WeightsParameterName,
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68 | "Array of weights used to generate the target values.",
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69 | (DoubleArray)(new DoubleArray(weights).AsReadOnly())));
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70 | Parameters.Add(new FixedValueParameter<DoubleValue>(
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71 | OptimalRSquaredParameterName,
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72 | "R² of the optimal solution.",
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73 | (DoubleValue)(new DoubleValue(optimalRSquared).AsReadOnly())));
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74 | }
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75 |
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76 | public override IDeepCloneable Clone(Cloner cloner) {
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77 | return new FeatureSelectionRegressionProblemData(this, cloner);
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78 | }
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79 | }
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80 | }
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