1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 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;
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24 | using System.Collections.Generic;
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25 | using System.Linq;
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26 | using System.Text;
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27 | using HeuristicLab.Common;
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28 | using HeuristicLab.Core;
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29 | using HeuristicLab.Data;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 |
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32 | namespace HeuristicLab.VariableInteractionNetworks {
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33 | [Item("VariableInteractionNetwork", "A graph representation of variables and their relationships.")]
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34 | [StorableClass]
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35 | public class VariableInteractionNetwork : DirectedGraph {
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36 | /// <summary>
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37 | /// Creates a network from a matrix of variable impacts (each row represents a target variable, each column represents an input variable)
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38 | /// The network is acyclic. Values in the diagonal are ignored.
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39 | /// The algorithm starts with an empty network and incrementally adds next most relevant input variable for each target variable up to a given threshold
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40 | /// In each iteration cycles are broken by removing the weakest link.
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41 | /// </summary>
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42 | /// <param name="nmse">vector of NMSE values for each target variable</param>
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43 | /// <param name="variableImpacts">Variable impacts (smaller is lower impact). Row names and columns names should be set</param>
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44 | /// <param name="nmseThreshold">Threshold for NMSE values. Variables with a NMSE value larger than the threshold are considered as independent variables</param>
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45 | /// <param name="varImpactThreshold">Threshold for variable impact values. Impacts with a value smaller than the threshold are considered as independent</param>
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46 | /// <returns></returns>
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47 | public static VariableInteractionNetwork FromNmseAndVariableImpacts(double[] nmse, DoubleMatrix variableImpacts, double nmseThreshold = 0.2, double varImpactThreshold = 0.0) {
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48 | if(variableImpacts.Rows != variableImpacts.Columns) throw new ArgumentException();
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49 | var network = new VariableInteractionNetwork();
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50 |
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51 | Dictionary<string, IVertex> name2funVertex = new Dictionary<string, IVertex>(); // store vertexes representing the function for each target so we can easily add incoming arcs later on
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52 | string[] varNames = variableImpacts.RowNames.ToArray();
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53 | if(nmse.Length != varNames.Length) throw new ArgumentException();
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54 |
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55 | for(int i = 0; i < varNames.Length; i++) {
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56 | var name = varNames[i];
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57 | var varVertex = new VariableNetworkNode() { Label = name };
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58 | network.AddVertex(varVertex);
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59 | if(nmse[i] < nmseThreshold) {
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60 | var functionVertex = new JunctionNetworkNode() { Label = "f_" + name };
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61 | name2funVertex.Add(name, functionVertex);
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62 | network.AddVertex(functionVertex);
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63 | var predArc = network.AddArc(functionVertex, varVertex);
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64 | predArc.Weight = double.PositiveInfinity; // never delete arcs from f_x -> x (representing output of a function)
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65 | }
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66 | }
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67 |
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68 | // rel is updated (impacts which are represented in the network are set to zero)
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69 | var rel = variableImpacts.CloneAsMatrix();
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70 | // make sure the diagonal is not considered
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71 | for(int i = 0; i < rel.GetLength(0); i++) rel[i, i] = double.NegativeInfinity;
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72 |
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73 | var addedArcs = AddArcs(network, rel, varNames, name2funVertex, varImpactThreshold);
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74 | while(addedArcs.Any()) {
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75 | var cycles = network.FindShortestCycles().ToList();
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76 | while(cycles.Any()) {
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77 | // delete weakest link
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78 | var weakestArc = cycles.SelectMany(cycle => network.ArcsForCycle(cycle)).OrderBy(a => a.Weight).First();
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79 | network.RemoveArc(weakestArc);
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80 |
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81 | cycles = network.FindShortestCycles().ToList();
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82 | }
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83 |
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84 | addedArcs = AddArcs(network, rel, varNames, name2funVertex, varImpactThreshold);
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85 | }
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86 |
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87 | return network;
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88 | }
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89 |
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90 | private static List<IArc> AddArcs(VariableInteractionNetwork network, double[,] impacts, string[] varNames, Dictionary<string, IVertex> name2funVertex, double threshold = 0.0) {
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91 | var newArcs = new List<IArc>();
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92 | for(int row = 0; row < impacts.GetLength(0); row++) {
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93 | if(!name2funVertex.ContainsKey(varNames[row])) continue; // this variable does not have an associated function (considered as independent)
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94 |
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95 | var rowVector = Enumerable.Range(0, impacts.GetLength(0)).Select(col => impacts[row, col]).ToArray();
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96 | var max = rowVector.Max();
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97 | if(max > threshold) {
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98 | var idxOfMax = Array.IndexOf<double>(rowVector, max);
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99 | impacts[row, idxOfMax] = double.NegativeInfinity; // edge is not considered anymore
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100 | var srcName = varNames[idxOfMax];
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101 | var dstName = varNames[row];
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102 | var vertex = network.Vertices.Single(v => v.Label == srcName);
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103 | var arc = network.AddArc(vertex, name2funVertex[dstName]);
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104 | arc.Weight = max;
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105 | newArcs.Add(arc);
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106 | }
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107 | }
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108 | return newArcs;
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109 | }
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110 |
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111 | [StorableConstructor]
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112 | public VariableInteractionNetwork(bool deserializing) : base(deserializing) { }
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113 |
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114 | public VariableInteractionNetwork() { }
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115 |
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116 | protected VariableInteractionNetwork(VariableInteractionNetwork original, Cloner cloner) : base(original, cloner) { }
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117 |
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118 | public override IDeepCloneable Clone(Cloner cloner) {
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119 | return new VariableInteractionNetwork(this, cloner);
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120 | }
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121 | private IList<IArc> ArcsForCycle(IList<IVertex> cycle) {
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122 | var res = new List<IArc>();
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123 | foreach(var t in cycle.Zip(cycle.Skip(1), Tuple.Create)) {
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124 | var src = t.Item1;
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125 | var dst = t.Item2;
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126 | var arc = Arcs.Single(a => a.Source == src && a.Target == dst);
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127 | res.Add(arc);
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128 | }
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129 | return res;
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130 | }
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131 |
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132 |
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133 | // finds the shortest cycles in the graph and returns all sub-graphs containing only the nodes / edges within the cycle
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134 | public IEnumerable<IList<IVertex>> FindShortestCycles() {
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135 | foreach(var startVariable in base.Vertices.OfType<VariableNetworkNode>()) {
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136 | foreach(var cycle in FindShortestCycles(startVariable))
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137 | yield return cycle;
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138 | }
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139 | }
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140 |
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141 | private IEnumerable<IList<IVertex>> FindShortestCycles(VariableNetworkNode startVariable) {
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142 | var q = new Queue<List<IVertex>>(); // queue of paths
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143 | var path = new List<IVertex>();
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144 | var cycles = new List<List<IVertex>>();
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145 | var maxPathLength = base.Vertices.Count();
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146 |
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147 | path.Add(startVariable);
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148 | q.Enqueue(new List<IVertex>(path));
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149 |
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150 | FindShortestCycles(q, maxPathLength, cycles);
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151 | return cycles;
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152 | }
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153 |
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154 | // TODO efficiency
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155 | private void FindShortestCycles(Queue<List<IVertex>> queue, int maxPathLength, List<List<IVertex>> cycles) {
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156 | while(queue.Any()) {
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157 | var path = queue.Dequeue();
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158 | if(path.Count > 1 && path.First() == path.Last()) {
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159 | cycles.Add(new List<IVertex>(path)); // found a cycle
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160 | } else if(path.Count >= maxPathLength) {
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161 | continue;
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162 | } else {
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163 | var lastVert = path.Last();
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164 | var neighbours = base.Arcs.Where(a => a.Source == lastVert).Select(a => a.Target);
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165 | foreach(var neighbour in neighbours) {
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166 | queue.Enqueue(new List<IVertex>(path.Concat(new IVertex[] { neighbour })));
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167 | }
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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 | public DoubleMatrix GetWeightsMatrix() {
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173 | var names = Vertices.OfType<VariableNetworkNode>()
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174 | .Select(v => v.Label)
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175 | .OrderBy(s => s, new NaturalStringComparer()).ToArray();
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176 | var w = new double[names.Length, names.Length];
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177 |
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178 | var name2idx = new Dictionary<string, int>();
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179 | for(int i = 0; i < names.Length; i++) {
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180 | name2idx.Add(names[i], i);
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181 | }
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182 |
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183 | foreach(var arc in Arcs) {
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184 | // only consider arcs going into a junction node
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185 | var target = arc.Target as JunctionNetworkNode;
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186 | if(target != null)
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187 | {
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188 | var srcVarName = arc.Source.Label;
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189 | // each function node must have exactly one outgoing arc
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190 | var dstVarName = arc.Target.OutArcs.Single().Target.Label;
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191 |
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192 | w[name2idx[dstVarName], name2idx[srcVarName]] = arc.Weight;
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193 | }
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194 | }
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195 |
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196 |
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197 | return new DoubleMatrix(w, names, names);
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198 | }
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199 |
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200 | public string ToGraphVizString() {
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201 | var sb = new StringBuilder();
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202 | sb.AppendLine("digraph {");
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203 | sb.AppendLine("rankdir=LR");
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204 | foreach(var v in Vertices.OfType<VariableNetworkNode>()) {
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205 | sb.AppendFormat("\"{0}\" [shape=oval]", v.Label).AppendLine();
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206 | }
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207 | foreach(var v in Vertices.OfType<JunctionNetworkNode>()) {
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208 | sb.AppendFormat("\"{0}\" [shape=box]", v.Label).AppendLine();
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209 | }
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210 | foreach(var arc in Arcs) {
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211 | sb.AppendFormat("\"{0}\"->\"{1}\"", arc.Source.Label, arc.Target.Label).AppendLine();
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212 | }
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213 | sb.AppendLine("}");
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214 | return sb.ToString();
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215 | }
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216 | }
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217 |
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218 | [Item("VariableNetworkNode", "A graph vertex which represents a symbolic regression variable.")]
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219 | [StorableClass]
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220 | public class VariableNetworkNode : Vertex<IDeepCloneable>, INetworkNode {
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221 | public VariableNetworkNode() {
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222 | Id = Guid.NewGuid().ToString();
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223 | }
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224 |
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225 | public VariableNetworkNode(VariableNetworkNode original, Cloner cloner) : base(original, cloner) {
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226 | Id = original.Id;
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227 | Description = original.Description;
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228 | }
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229 |
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230 | public override IDeepCloneable Clone(Cloner cloner) {
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231 | return new VariableNetworkNode(this, cloner);
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232 | }
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233 |
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234 | public string Id { get; }
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235 | public string Description { get; set; }
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236 | }
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237 |
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238 | [Item("FunctionNetworkNode", "A graph vertex representing a junction node.")]
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239 | [StorableClass]
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240 | public class JunctionNetworkNode : Vertex<IDeepCloneable>, INetworkNode {
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241 | public JunctionNetworkNode() {
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242 | Id = Guid.NewGuid().ToString();
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243 | }
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244 |
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245 | public JunctionNetworkNode(JunctionNetworkNode original, Cloner cloner) : base(original, cloner) {
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246 | Id = original.Id;
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247 | Description = original.Description;
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248 | }
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249 |
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250 | public override IDeepCloneable Clone(Cloner cloner) {
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251 | return new JunctionNetworkNode(this, cloner);
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252 | }
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253 |
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254 | public string Id { get; }
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255 | public string Description { get; set; }
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256 | }
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257 | }
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