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 System.Threading;
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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.Optimization;
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30 | using HeuristicLab.Parameters;
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31 | using HeuristicLab.Persistence;
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32 | using HeuristicLab.Problems.DataAnalysis;
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33 |
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34 | namespace HeuristicLab.Algorithms.DataAnalysis {
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35 | /// <summary>
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36 | /// Neural network regression data analysis algorithm.
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37 | /// </summary>
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38 | [Item("Neural Network Regression (NN)", "Neural network regression data analysis algorithm (wrapper for ALGLIB). Further documentation: http://www.alglib.net/dataanalysis/neuralnetworks.php")]
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39 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 130)]
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40 | [StorableType("a7a3987c-f555-4c79-b22c-a2d50b42884f")]
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41 | public sealed class NeuralNetworkRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
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42 | private const string DecayParameterName = "Decay";
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43 | private const string HiddenLayersParameterName = "HiddenLayers";
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44 | private const string NodesInFirstHiddenLayerParameterName = "NodesInFirstHiddenLayer";
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45 | private const string NodesInSecondHiddenLayerParameterName = "NodesInSecondHiddenLayer";
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46 | private const string RestartsParameterName = "Restarts";
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47 | private const string NeuralNetworkRegressionModelResultName = "Neural network regression solution";
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48 |
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49 | #region parameter properties
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50 | public IFixedValueParameter<DoubleValue> DecayParameter {
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51 | get { return (IFixedValueParameter<DoubleValue>)Parameters[DecayParameterName]; }
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52 | }
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53 | public IConstrainedValueParameter<IntValue> HiddenLayersParameter {
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54 | get { return (IConstrainedValueParameter<IntValue>)Parameters[HiddenLayersParameterName]; }
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55 | }
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56 | public IFixedValueParameter<IntValue> NodesInFirstHiddenLayerParameter {
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57 | get { return (IFixedValueParameter<IntValue>)Parameters[NodesInFirstHiddenLayerParameterName]; }
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58 | }
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59 | public IFixedValueParameter<IntValue> NodesInSecondHiddenLayerParameter {
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60 | get { return (IFixedValueParameter<IntValue>)Parameters[NodesInSecondHiddenLayerParameterName]; }
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61 | }
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62 | public IFixedValueParameter<IntValue> RestartsParameter {
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63 | get { return (IFixedValueParameter<IntValue>)Parameters[RestartsParameterName]; }
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64 | }
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65 | #endregion
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66 |
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67 | #region properties
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68 | public double Decay {
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69 | get { return DecayParameter.Value.Value; }
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70 | set {
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71 | if (value < 0.001 || value > 100) throw new ArgumentException("The decay parameter should be set to a value between 0.001 and 100.", "Decay");
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72 | DecayParameter.Value.Value = value;
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73 | }
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74 | }
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75 | public int HiddenLayers {
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76 | get { return HiddenLayersParameter.Value.Value; }
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77 | set {
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78 | if (value < 0 || value > 2) throw new ArgumentException("The number of hidden layers should be set to 0, 1, or 2.", "HiddenLayers");
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79 | HiddenLayersParameter.Value = (from v in HiddenLayersParameter.ValidValues
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80 | where v.Value == value
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81 | select v)
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82 | .Single();
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83 | }
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84 | }
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85 | public int NodesInFirstHiddenLayer {
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86 | get { return NodesInFirstHiddenLayerParameter.Value.Value; }
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87 | set {
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88 | if (value < 1) throw new ArgumentException("The number of nodes in the first hidden layer must be at least one.", "NodesInFirstHiddenLayer");
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89 | NodesInFirstHiddenLayerParameter.Value.Value = value;
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90 | }
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91 | }
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92 | public int NodesInSecondHiddenLayer {
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93 | get { return NodesInSecondHiddenLayerParameter.Value.Value; }
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94 | set {
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95 | if (value < 1) throw new ArgumentException("The number of nodes in the first second layer must be at least one.", "NodesInSecondHiddenLayer");
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96 | NodesInSecondHiddenLayerParameter.Value.Value = value;
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97 | }
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98 | }
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99 | public int Restarts {
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100 | get { return RestartsParameter.Value.Value; }
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101 | set {
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102 | if (value < 0) throw new ArgumentException("The number of restarts must be positive.", "Restarts");
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103 | RestartsParameter.Value.Value = value;
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104 | }
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105 | }
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106 | #endregion
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107 |
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108 |
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109 | [StorableConstructor]
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110 | private NeuralNetworkRegression(StorableConstructorFlag deserializing) : base(deserializing) { }
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111 | private NeuralNetworkRegression(NeuralNetworkRegression original, Cloner cloner)
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112 | : base(original, cloner) {
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113 | RegisterEventHandlers();
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114 | }
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115 | public NeuralNetworkRegression()
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116 | : base() {
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117 | var validHiddenLayerValues = new ItemSet<IntValue>(new IntValue[] {
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118 | (IntValue)new IntValue(0).AsReadOnly(),
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119 | (IntValue)new IntValue(1).AsReadOnly(),
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120 | (IntValue)new IntValue(2).AsReadOnly() });
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121 | var selectedHiddenLayerValue = (from v in validHiddenLayerValues
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122 | where v.Value == 1
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123 | select v)
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124 | .Single();
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125 | Parameters.Add(new FixedValueParameter<DoubleValue>(DecayParameterName, "The decay parameter for the training phase of the neural network. This parameter determines the strengh of regularization and should be set to a value between 0.001 (weak regularization) to 100 (very strong regularization). The correct value should be determined via cross-validation.", new DoubleValue(1)));
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126 | Parameters.Add(new ConstrainedValueParameter<IntValue>(HiddenLayersParameterName, "The number of hidden layers for the neural network (0, 1, or 2)", validHiddenLayerValues, selectedHiddenLayerValue));
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127 | Parameters.Add(new FixedValueParameter<IntValue>(NodesInFirstHiddenLayerParameterName, "The number of nodes in the first hidden layer. This value is not used if the number of hidden layers is zero.", new IntValue(10)));
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128 | Parameters.Add(new FixedValueParameter<IntValue>(NodesInSecondHiddenLayerParameterName, "The number of nodes in the second hidden layer. This value is not used if the number of hidden layers is zero or one.", new IntValue(10)));
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129 | Parameters.Add(new FixedValueParameter<IntValue>(RestartsParameterName, "The number of restarts for learning.", new IntValue(2)));
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130 |
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131 | RestartsParameter.Hidden = true;
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132 | NodesInSecondHiddenLayerParameter.Hidden = true;
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133 |
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134 | RegisterEventHandlers();
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135 |
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136 | Problem = new RegressionProblem();
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137 | }
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138 |
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139 | private void RegisterEventHandlers() {
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140 | HiddenLayersParameter.Value.ValueChanged += HiddenLayersParameterValueValueChanged;
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141 | HiddenLayersParameter.ValueChanged += HiddenLayersParameterValueChanged;
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142 | }
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143 |
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144 | [StorableHook(HookType.AfterDeserialization)]
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145 | private void AfterDeserialization() {
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146 | RegisterEventHandlers();
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147 | }
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148 |
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149 | public override IDeepCloneable Clone(Cloner cloner) {
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150 | return new NeuralNetworkRegression(this, cloner);
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151 | }
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152 |
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153 | private void HiddenLayersParameterValueChanged(object source, EventArgs e) {
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154 | HiddenLayersParameter.Value.ValueChanged += HiddenLayersParameterValueValueChanged;
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155 | HiddenLayersParameterValueValueChanged(this, EventArgs.Empty);
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156 | }
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157 |
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158 | private void HiddenLayersParameterValueValueChanged(object source, EventArgs e) {
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159 | if (HiddenLayers == 0) {
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160 | NodesInFirstHiddenLayerParameter.Hidden = true;
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161 | NodesInSecondHiddenLayerParameter.Hidden = true;
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162 | } else if (HiddenLayers == 1) {
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163 | NodesInFirstHiddenLayerParameter.Hidden = false;
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164 | NodesInSecondHiddenLayerParameter.Hidden = true;
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165 | } else {
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166 | NodesInFirstHiddenLayerParameter.Hidden = false;
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167 | NodesInSecondHiddenLayerParameter.Hidden = false;
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168 | }
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169 | }
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170 |
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171 |
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172 | #region neural network
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173 | protected override void Run(CancellationToken cancellationToken) {
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174 | double rmsError, avgRelError;
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175 | var solution = CreateNeuralNetworkRegressionSolution(Problem.ProblemData, HiddenLayers, NodesInFirstHiddenLayer, NodesInSecondHiddenLayer, Decay, Restarts, out rmsError, out avgRelError);
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176 | Results.Add(new Result(NeuralNetworkRegressionModelResultName, "The neural network regression solution.", solution));
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177 | Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network regression solution on the training set.", new DoubleValue(rmsError)));
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178 | Results.Add(new Result("Average relative error", "The average of relative errors of the neural network regression solution on the training set.", new PercentValue(avgRelError)));
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179 | }
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180 |
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181 | public static IRegressionSolution CreateNeuralNetworkRegressionSolution(IRegressionProblemData problemData, int nLayers, int nHiddenNodes1, int nHiddenNodes2, double decay, int restarts,
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182 | out double rmsError, out double avgRelError) {
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183 | var dataset = problemData.Dataset;
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184 | string targetVariable = problemData.TargetVariable;
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185 | IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
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186 | IEnumerable<int> rows = problemData.TrainingIndices;
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187 | double[,] inputMatrix = dataset.ToArray(allowedInputVariables.Concat(new string[] { targetVariable }), rows);
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188 | if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
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189 | throw new NotSupportedException("Neural network regression does not support NaN or infinity values in the input dataset.");
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190 |
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191 | alglib.multilayerperceptron multiLayerPerceptron = null;
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192 | if (nLayers == 0) {
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193 | alglib.mlpcreate0(allowedInputVariables.Count(), 1, out multiLayerPerceptron);
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194 | } else if (nLayers == 1) {
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195 | alglib.mlpcreate1(allowedInputVariables.Count(), nHiddenNodes1, 1, out multiLayerPerceptron);
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196 | } else if (nLayers == 2) {
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197 | alglib.mlpcreate2(allowedInputVariables.Count(), nHiddenNodes1, nHiddenNodes2, 1, out multiLayerPerceptron);
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198 | } else throw new ArgumentException("Number of layers must be zero, one, or two.", "nLayers");
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199 | alglib.mlpreport rep;
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200 | int nRows = inputMatrix.GetLength(0);
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201 |
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202 | int info;
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203 | // using mlptrainlm instead of mlptraines or mlptrainbfgs because only one parameter is necessary
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204 | alglib.mlptrainlm(multiLayerPerceptron, inputMatrix, nRows, decay, restarts, out info, out rep);
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205 | if (info != 2) throw new ArgumentException("Error in calculation of neural network regression solution");
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206 |
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207 | rmsError = alglib.mlprmserror(multiLayerPerceptron, inputMatrix, nRows);
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208 | avgRelError = alglib.mlpavgrelerror(multiLayerPerceptron, inputMatrix, nRows);
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209 |
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210 | return new NeuralNetworkRegressionSolution(new NeuralNetworkModel(multiLayerPerceptron, targetVariable, allowedInputVariables), (IRegressionProblemData)problemData.Clone());
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211 | }
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212 | #endregion
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213 | }
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214 | }
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