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.Linq;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Persistence;
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28 | using HeuristicLab.Problems.DataAnalysis;
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29 |
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30 | namespace HeuristicLab.Algorithms.DataAnalysis {
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31 | [StorableType("67b1e15e-6954-47b2-8123-cbf764881e7c")]
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32 | [Item("OneR Classification Model", "A model that uses intervals for one variable to determine the class.")]
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33 | public sealed class OneRClassificationModel : ClassificationModel {
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34 | public override IEnumerable<string> VariablesUsedForPrediction {
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35 | get { return new[] { Variable }; }
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36 | }
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37 |
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38 | [Storable]
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39 | private string variable;
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40 | public string Variable {
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41 | get { return variable; }
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42 | }
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43 |
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44 | [Storable]
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45 | private double[] splits;
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46 | public double[] Splits {
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47 | get { return splits; }
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48 | }
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49 |
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50 | [Storable]
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51 | private double[] classes;
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52 | public double[] Classes {
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53 | get { return classes; }
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54 | }
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55 |
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56 | [Storable]
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57 | private double missingValuesClass;
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58 | public double MissingValuesClass {
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59 | get { return missingValuesClass; }
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60 | }
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61 |
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62 | [StorableConstructor]
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63 | private OneRClassificationModel(StorableConstructorFlag deserializing) : base(deserializing) { }
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64 | private OneRClassificationModel(OneRClassificationModel original, Cloner cloner)
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65 | : base(original, cloner) {
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66 | this.variable = (string)original.variable;
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67 | this.splits = (double[])original.splits.Clone();
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68 | this.classes = (double[])original.classes.Clone();
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69 | this.missingValuesClass = original.missingValuesClass;
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70 | }
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71 | public override IDeepCloneable Clone(Cloner cloner) { return new OneRClassificationModel(this, cloner); }
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72 |
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73 | public OneRClassificationModel(string targetVariable, string variable, double[] splits, double[] classes, double missingValuesClass = double.NaN)
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74 | : base(targetVariable) {
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75 | if (splits.Length != classes.Length) {
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76 | throw new ArgumentException("Number of splits and classes has to be equal.");
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77 | }
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78 | if (!Double.IsPositiveInfinity(splits[splits.Length - 1])) {
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79 | throw new ArgumentException("Last split has to be double.PositiveInfinity, so that all values are covered.");
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80 | }
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81 | this.name = ItemName;
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82 | this.description = ItemDescription;
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83 | this.variable = variable;
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84 | this.splits = splits;
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85 | this.classes = classes;
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86 | this.missingValuesClass = missingValuesClass;
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87 | }
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88 |
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89 | // uses sorting to return the values in the order of rows, instead of using nested for loops
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90 | // to avoid O(nĀ²) runtime
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91 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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92 | var values = dataset.GetDoubleValues(Variable, rows).ToArray();
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93 | var rowsArray = rows.ToArray();
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94 | var order = Enumerable.Range(0, rowsArray.Length).ToArray();
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95 | double[] estimated = new double[rowsArray.Length];
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96 | Array.Sort(rowsArray, order);
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97 | Array.Sort(values, rowsArray);
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98 | int curSplit = 0, curIndex = 0;
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99 | while (curIndex < values.Length && Double.IsNaN(values[curIndex])) {
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100 | estimated[curIndex] = MissingValuesClass;
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101 | curIndex++;
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102 | }
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103 | while (curSplit < Splits.Length) {
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104 | while (curIndex < values.Length && Splits[curSplit] > values[curIndex]) {
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105 | estimated[curIndex] = classes[curSplit];
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106 | curIndex++;
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107 | }
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108 | curSplit++;
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109 | }
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110 | Array.Sort(rowsArray, estimated);
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111 | Array.Sort(order, estimated);
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112 | return estimated;
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113 | }
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114 |
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115 | public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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116 | return new OneRClassificationSolution(this, new ClassificationProblemData(problemData));
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117 | }
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118 |
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119 | }
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120 | }
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