1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 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 HEAL.Attic;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Data;
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29 | using HeuristicLab.Parameters;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis {
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32 | [StorableType("8D44EABE-2D52-4501-B62D-5E28FB4CFEAE")]
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33 | [Item("ShapeConstrainedProblemData", "Represents an item containing all data defining a regression problem with shape constraints.")]
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34 | public class ShapeConstrainedRegressionProblemData : RegressionProblemData, IShapeConstrainedRegressionProblemData {
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35 | protected const string ShapeConstraintsParameterName = "ShapeConstraints";
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36 |
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37 | #region default data
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38 | private static double[,] sigmoid = new double[,] {
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39 | {1.00, 0.09, 0.01390952},
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40 | {1.10, 0.11, 0.048256016},
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41 | {1.20, 0.14, 0.010182641},
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42 | {1.30, 0.17, 0.270361269},
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43 | {1.40, 0.20, 0.091503971},
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44 | {1.50, 0.24, 0.338157191},
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45 | {1.60, 0.28, 0.328508579},
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46 | {1.70, 0.34, 0.21867684},
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47 | {1.80, 0.40, 0.34515433},
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48 | {1.90, 0.46, 0.562746903},
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49 | {2.00, 0.54, 0.554800831},
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50 | {2.10, 0.62, 0.623018787},
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51 | {2.20, 0.71, 0.626224329},
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52 | {2.30, 0.80, 0.909006688},
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53 | {2.40, 0.90, 0.92514929},
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54 | {2.50, 1.00, 1.097199936},
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55 | {2.60, 1.10, 1.138309608},
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56 | {2.70, 1.20, 1.087880692},
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57 | {2.80, 1.29, 1.370491683},
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58 | {2.90, 1.38, 1.422048792},
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59 | {3.00, 1.46, 1.505242141},
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60 | {3.10, 1.54, 1.684790135},
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61 | {3.20, 1.60, 1.480232277},
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62 | {3.30, 1.66, 1.577412501},
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63 | {3.40, 1.72, 1.664822534},
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64 | {3.50, 1.76, 1.773580664},
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65 | {3.60, 1.80, 1.941034478},
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66 | {3.70, 1.83, 1.730361986},
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67 | {3.80, 1.86, 1.9785952},
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68 | {3.90, 1.89, 1.946698641},
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69 | {4.00, 1.91, 1.766502803},
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70 | {4.10, 1.92, 1.847756843},
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71 | {4.20, 1.94, 1.894506213},
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72 | {4.30, 1.95, 2.029194724},
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73 | {4.40, 1.96, 2.01830679},
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74 | {4.50, 1.96, 1.924316332},
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75 | {4.60, 1.97, 1.971354792},
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76 | {4.70, 1.98, 1.85665728},
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77 | {4.80, 1.98, 1.831400496},
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78 | {4.90, 1.98, 2.057843156},
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79 | {5.00, 1.99, 2.128769896},
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80 | };
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81 |
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82 | private static readonly Dataset defaultDataset;
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83 | private static readonly IEnumerable<string> defaultAllowedInputVariables;
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84 | private static readonly string defaultTargetVariable;
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85 | private static readonly ShapeConstraints defaultShapeConstraints;
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86 | private static readonly IntervalCollection defaultVariableRanges;
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87 |
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88 | private static readonly ShapeConstrainedRegressionProblemData emptyProblemData;
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89 | public new static ShapeConstrainedRegressionProblemData EmptyProblemData => emptyProblemData;
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90 |
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91 | static ShapeConstrainedRegressionProblemData() {
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92 | defaultDataset = new Dataset(new string[] { "x", "y", "y_noise" }, sigmoid) {
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93 | Name = "Sigmoid function for shape-constrained symbolic regression.",
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94 | Description = "f(x) = 1 + tanh(x - 2.5)"
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95 | };
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96 | defaultAllowedInputVariables = new List<string>() { "x" };
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97 | defaultTargetVariable = "y_noise";
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98 | defaultShapeConstraints = new ShapeConstraints {
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99 | new ShapeConstraint(new Interval(0, 2), 1.0),
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100 | new ShapeConstraint("x", 1, new Interval(0, double.PositiveInfinity), 1.0)
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101 | };
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102 | defaultVariableRanges = defaultDataset.GetVariableRanges();
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103 | defaultVariableRanges.SetInterval("x", new Interval(0, 6));
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104 |
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105 | var problemData = new ShapeConstrainedRegressionProblemData();
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106 | problemData.Parameters.Clear();
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107 | problemData.Name = "Empty Regression ProblemData";
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108 | problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
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109 | problemData.isEmpty = true;
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110 |
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111 | problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
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112 | problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
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113 | problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 20).AsReadOnly()));
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114 | problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(20, 40).AsReadOnly()));
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115 | problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
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116 | problemData.Parameters.Add(new FixedValueParameter<IntervalCollection>(VariableRangesParameterName, "", new IntervalCollection()));
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117 | problemData.Parameters.Add(new FixedValueParameter<ShapeConstraints>(ShapeConstraintsParameterName, "", new ShapeConstraints()));
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118 | emptyProblemData = problemData;
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119 | }
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120 | #endregion
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121 |
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122 | public IFixedValueParameter<ShapeConstraints> ShapeConstraintParameter => (IFixedValueParameter<ShapeConstraints>)Parameters[ShapeConstraintsParameterName];
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123 | public ShapeConstraints ShapeConstraints => ShapeConstraintParameter.Value;
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124 |
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125 | [StorableConstructor]
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126 | protected ShapeConstrainedRegressionProblemData(StorableConstructorFlag _) : base(_) { }
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127 |
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128 | protected ShapeConstrainedRegressionProblemData(ShapeConstrainedRegressionProblemData original, Cloner cloner) : base(original, cloner) {
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129 | RegisterEventHandlers();
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130 | }
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131 |
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132 | [StorableHook(HookType.AfterDeserialization)]
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133 | private void AfterDeserialization() {
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134 | RegisterEventHandlers();
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135 | }
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136 | public override IDeepCloneable Clone(Cloner cloner) {
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137 | return this == emptyProblemData ? emptyProblemData : new ShapeConstrainedRegressionProblemData(this, cloner);
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138 | }
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139 |
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140 | public ShapeConstrainedRegressionProblemData() : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable,
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141 | trainingPartition: new IntRange(0, defaultDataset.Rows),
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142 | testPartition: new IntRange(0, 0), sc: defaultShapeConstraints, variableRanges: defaultVariableRanges) { } // no test partition for the demo problem
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143 |
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144 | public ShapeConstrainedRegressionProblemData(IRegressionProblemData regressionProblemData)
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145 | : this(regressionProblemData.Dataset, regressionProblemData.AllowedInputVariables, regressionProblemData.TargetVariable,
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146 | regressionProblemData.TrainingPartition, regressionProblemData.TestPartition, regressionProblemData.Transformations,
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147 | (regressionProblemData is ShapeConstrainedRegressionProblemData) ? ((ShapeConstrainedRegressionProblemData)regressionProblemData).ShapeConstraints : null,
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148 | regressionProblemData.VariableRanges) {
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149 | }
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150 |
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151 | public ShapeConstrainedRegressionProblemData(IDataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable,
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152 | IntRange trainingPartition, IntRange testPartition,
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153 | IEnumerable<ITransformation> transformations = null, ShapeConstraints sc = null, IntervalCollection variableRanges = null)
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154 | : base(dataset, allowedInputVariables, targetVariable, transformations ?? Enumerable.Empty<ITransformation>(), variableRanges) {
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155 | TrainingPartition.Start = trainingPartition.Start;
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156 | TrainingPartition.End = trainingPartition.End;
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157 | TestPartition.Start = testPartition.Start;
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158 | TestPartition.End = testPartition.End;
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159 | if (sc == null) sc = new ShapeConstraints();
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160 | Parameters.Add(new FixedValueParameter<ShapeConstraints>(ShapeConstraintsParameterName, "Specifies the shape constraints for the regression problem.", (ShapeConstraints)sc.Clone()));
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161 |
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162 | RegisterEventHandlers();
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163 | }
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164 |
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165 | private void RegisterEventHandlers() {
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166 | ShapeConstraints.Changed += ShapeConstraints_Changed;
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167 | ShapeConstraints.CheckedItemsChanged += ShapeConstraints_Changed;
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168 | ShapeConstraints.CollectionReset += ShapeConstraints_Changed;
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169 | ShapeConstraints.ItemsAdded += ShapeConstraints_Changed;
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170 | ShapeConstraints.ItemsRemoved += ShapeConstraints_Changed;
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171 | ShapeConstraints.ItemsMoved += ShapeConstraints_Changed;
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172 | ShapeConstraints.ItemsReplaced += ShapeConstraints_Changed;
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173 | }
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174 |
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175 | private void ShapeConstraints_Changed(object sender, EventArgs e) {
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176 | OnChanged();
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177 | }
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178 | }
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179 | }
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