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 System.Diagnostics.Eventing.Reader;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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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.Algorithms.DataAnalysis {
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32 | [StorableClass]
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33 | [Item("OneFactor Classification Model", "A model that uses only one categorical feature (factor) to determine the class.")]
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34 | public class OneFactorClassificationModel : ClassificationModel {
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35 | public override IEnumerable<string> VariablesUsedForPrediction {
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36 | get { return new[] { Variable }; }
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37 | }
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38 |
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39 | [Storable]
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40 | protected string variable;
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41 | public string Variable {
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42 | get { return variable; }
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43 | }
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44 |
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45 | [Storable]
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46 | protected string[] variableValues;
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47 | public string[] VariableValues {
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48 | get { return variableValues; }
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49 | }
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50 |
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51 | [Storable]
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52 | protected double[] classes;
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53 | public double[] Classes {
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54 | get { return classes; }
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55 | }
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56 |
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57 | [Storable]
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58 | protected double defaultClass;
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59 | public double DefaultClass {
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60 | get { return defaultClass; }
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61 | }
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62 |
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63 | [StorableConstructor]
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64 | protected OneFactorClassificationModel(bool deserializing) : base(deserializing) { }
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65 | protected OneFactorClassificationModel(OneFactorClassificationModel original, Cloner cloner)
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66 | : base(original, cloner) {
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67 | this.variable = (string)original.variable;
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68 | this.variableValues = (string[])original.variableValues.Clone();
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69 | this.classes = (double[])original.classes.Clone();
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70 | this.defaultClass = original.defaultClass;
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71 | }
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72 | public override IDeepCloneable Clone(Cloner cloner) { return new OneFactorClassificationModel(this, cloner); }
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73 |
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74 | public OneFactorClassificationModel(string targetVariable, string variable, string[] variableValues, double[] classes, double defaultClass = double.NaN)
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75 | : base(targetVariable) {
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76 | if (variableValues.Length != classes.Length) {
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77 | throw new ArgumentException("Number of variable values and classes has to be equal.");
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78 | }
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79 | this.name = ItemName;
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80 | this.description = ItemDescription;
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81 | this.variable = variable;
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82 | this.variableValues = variableValues;
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83 | this.classes = classes;
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84 | this.defaultClass = double.IsNaN(defaultClass) ? classes.First() : defaultClass;
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85 | Array.Sort(variableValues, classes);
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86 | }
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87 |
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88 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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89 | return dataset.GetStringValues(Variable, rows)
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90 | .Select(GetPredictedValueForInput);
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91 | }
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92 |
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93 | private double GetPredictedValueForInput(string val) {
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94 | var matchingIdx = Array.BinarySearch(variableValues, val);
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95 | if (matchingIdx >= 0) return classes[matchingIdx];
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96 | else return DefaultClass;
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97 | }
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98 |
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99 | public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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100 | return new OneFactorClassificationSolution(this, new ClassificationProblemData(problemData));
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101 | }
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102 |
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103 | }
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104 | }
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