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.Linq;
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24 | using System.Threading;
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25 | using HeuristicLab.Algorithms.DataAnalysis;
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26 | using HeuristicLab.Algorithms.GeneticAlgorithm;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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29 | using HeuristicLab.Core.Networks;
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30 | using HeuristicLab.Data;
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31 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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32 | using HeuristicLab.Networks.FeatureSelection_Network;
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33 | using HeuristicLab.Networks.Programmable;
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34 | using HeuristicLab.Problems.DataAnalysis;
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35 | using HeuristicLab.Problems.Instances.DataAnalysis;
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36 | using HeuristicLab.Random;
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37 |
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38 | namespace HeuristicLab.Networks {
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39 | [Creatable("Optimization Networks")]
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40 | [Item("Feature Selection Network 2", "An optimization network which contains a binary GA and a LR.")]
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41 | public sealed class CompiledFeatureSelectionNetwork : Network {
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42 | private CompiledFeatureSelectionNetwork(CompiledFeatureSelectionNetwork original, Cloner cloner) : base(original, cloner) { }
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43 | public CompiledFeatureSelectionNetwork()
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44 | : base() {
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45 | if (Nodes.Count == 0)
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46 | Initialize();
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47 | }
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48 |
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49 | public override IDeepCloneable Clone(Cloner cloner) {
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50 | return new CompiledFeatureSelectionNetwork(this, cloner);
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51 | }
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52 |
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53 | public void Initialize() {
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54 |
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55 |
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56 | #region ParametersNode
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57 | var paramsNode = new UserDefinedNode("ParametersNode");
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58 | Nodes.Add(paramsNode);
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59 |
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60 | var paramsPort = new MessagePort("Parameters");
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61 | paramsNode.Ports.Add(paramsPort);
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62 |
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63 | var rand = new MersenneTwister();
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64 | var normRand = new NormalDistributedRandom(rand, 0, 1);
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65 | var instanceProvider = new FeatureSelectionInstanceProvider();
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66 | var problemData = instanceProvider.LoadData(new FeatureSelection(100, 0.1, 0.2, rand, normRand));
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67 |
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68 | paramsPort.Parameters.Add(new PortParameter<IRegressionProblemData>("ProblemData") {
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69 | Type = PortParameterType.Output,
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70 | DefaultValue = problemData
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71 | });
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72 | paramsPort.Parameters.Add(new PortParameter<IntValue>("Length") {
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73 | Type = PortParameterType.Output,
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74 | DefaultValue = new IntValue(problemData.AllowedInputVariables.Count())
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75 | });
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76 | #endregion
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77 |
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78 | #region Selection Node
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79 | var selectionNode = new AlgorithmNode("SelectionNode");
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80 | Nodes.Add(selectionNode);
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81 |
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82 | var configPort = new ConfigurationPort("ConfigureSelection");
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83 | selectionNode.Ports.Add(configPort);
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84 |
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85 | configPort.Parameters.Add(new PortParameter<IRegressionProblemData>("ProblemData") {
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86 | Type = PortParameterType.Input
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87 | });
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88 |
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89 | var evaluatePort = new MessagePort("EvaluateSelection");
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90 | selectionNode.Ports.Add(evaluatePort);
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91 |
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92 | evaluatePort.Parameters.Add(new PortParameter<BinaryVector>("Selection") {
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93 | Type = PortParameterType.Output
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94 | });
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95 | evaluatePort.Parameters.Add(new PortParameter<DoubleValue>("Quality") {
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96 | Type = PortParameterType.Output | PortParameterType.Input
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97 | });
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98 |
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99 | Func<BinaryVector, double> eval = vector => {
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100 | // hook returns test quality!, LR optimizes training quality only!
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101 |
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102 | var msg = evaluatePort.PrepareMessage();
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103 | msg["Selection"] = vector;
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104 | evaluatePort.SendMessage(msg, new CancellationToken(false));
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105 |
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106 | return ((DoubleValue)msg["Quality"]).Value;
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107 | };
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108 |
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109 | var selectionGa = new GeneticAlgorithm();
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110 | var selectionProb = new SelectionProblem(eval, false);
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111 |
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112 | selectionGa.Problem = selectionProb;
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113 | selectionGa.MaximumGenerations.Value = 50;
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114 | selectionGa.PopulationSize.Value = 10;
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115 | selectionGa.Mutator = selectionGa.MutatorParameter.ValidValues.Where(x => x.Name == "SinglePositionBitflipManipulator").First();
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116 | selectionNode.Algorithm = selectionGa;
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117 |
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118 | #endregion
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119 |
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120 | #region RegressionNode
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121 | var regressionNode = new AlgorithmNode("Regression");
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122 | Nodes.Add(regressionNode);
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123 |
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124 | var executePort = new ExecutionPort("Execute");
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125 | regressionNode.Ports.Add(executePort);
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126 |
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127 | executePort.Parameters.Add(new PortParameter<IRegressionProblemData>("ProblemData") {
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128 | Type = PortParameterType.Input
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129 | });
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130 | executePort.Parameters.Add(new PortParameter<IRegressionSolution>("Linear regression solution") {
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131 | Type = PortParameterType.Output
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132 | });
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133 |
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134 | var linReg = new LinearRegression();
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135 | regressionNode.Algorithm = linReg;
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136 | #endregion
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137 |
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138 | #region Connector
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139 | var featureSelectionConnector = (INode)new FeatureSelectionConnectorCode();
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140 | Nodes.Add(featureSelectionConnector);
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141 | #endregion
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142 |
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143 | #region Wire Ports
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144 | configPort.ConnectedPort = paramsPort;
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145 | evaluatePort.ConnectedPort = (IMessagePort)featureSelectionConnector.Ports["Selection Connector"];
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146 | ((IMessagePort)featureSelectionConnector.Ports["Parameters"]).ConnectedPort = paramsPort;
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147 | ((IMessagePort)featureSelectionConnector.Ports["Regression Connector"]).ConnectedPort = executePort;
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148 |
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149 | paramsPort.SendMessage(paramsPort.PrepareMessage());
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150 | #endregion
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151 | }
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152 | }
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153 | }
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