[11219] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
| 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
| 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
| 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
[11486] | 24 | using System.Diagnostics;
|
---|
| 25 | using System.Globalization;
|
---|
[11219] | 26 | using System.Linq;
|
---|
| 27 | using HeuristicLab.Common;
|
---|
| 28 | using HeuristicLab.Core;
|
---|
| 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
| 30 | using HeuristicLab.Optimization.Operators;
|
---|
| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 32 |
|
---|
[11221] | 33 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
|
---|
[11219] | 34 | [StorableClass]
|
---|
[11486] | 35 | [Item("BottomUpSimilarityCalculator", "A similarity calculator which uses the tree bottom-up distance as a similarity metric.")]
|
---|
| 36 | public class BottomUpSimilarityCalculator : SingleObjectiveSolutionSimilarityCalculator {
|
---|
| 37 | private readonly HashSet<string> commutativeSymbols = new HashSet<string> { "Addition", "Multiplication", "Average", "And", "Or", "Xor" };
|
---|
| 38 | public bool MatchVariableWeights { get; set; }
|
---|
| 39 | public bool MatchConstantValues { get; set; }
|
---|
[11219] | 40 |
|
---|
[11486] | 41 | public BottomUpSimilarityCalculator() { }
|
---|
[11219] | 42 |
|
---|
[11486] | 43 | protected BottomUpSimilarityCalculator(BottomUpSimilarityCalculator original, Cloner cloner)
|
---|
| 44 | : base(original, cloner) {
|
---|
[11239] | 45 | }
|
---|
| 46 |
|
---|
[11219] | 47 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
[11486] | 48 | return new BottomUpSimilarityCalculator(this, cloner);
|
---|
[11219] | 49 | }
|
---|
| 50 |
|
---|
[11486] | 51 | public double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) {
|
---|
| 52 | if (t1 == t2)
|
---|
| 53 | return 1;
|
---|
| 54 |
|
---|
| 55 | var map = ComputeBottomUpMapping(t1.Root, t2.Root);
|
---|
| 56 | return 2.0 * map.Count / (t1.Length + t2.Length);
|
---|
[11219] | 57 | }
|
---|
| 58 |
|
---|
| 59 | public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) {
|
---|
| 60 | var t1 = leftSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree;
|
---|
| 61 | var t2 = rightSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree;
|
---|
| 62 |
|
---|
| 63 | if (t1 == null || t2 == null)
|
---|
| 64 | throw new ArgumentException("Cannot calculate similarity when one of the arguments is null.");
|
---|
| 65 |
|
---|
[11486] | 66 | var similarity = CalculateSimilarity(t1, t2);
|
---|
[11224] | 67 | if (similarity > 1.0)
|
---|
| 68 | throw new Exception("Similarity value cannot be greater than 1");
|
---|
| 69 |
|
---|
| 70 | return similarity;
|
---|
[11219] | 71 | }
|
---|
| 72 |
|
---|
[11221] | 73 | public Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode> ComputeBottomUpMapping(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
|
---|
[11486] | 74 | var comparer = new SymbolicExpressionTreeNodeComparer(); // use a node comparer because it's faster than calling node.ToString() (strings are expensive) and comparing strings
|
---|
[11221] | 75 | var compactedGraph = Compact(n1, n2);
|
---|
[11219] | 76 |
|
---|
| 77 | var forwardMap = new Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode>(); // nodes of t1 => nodes of t2
|
---|
| 78 | var reverseMap = new Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode>(); // nodes of t2 => nodes of t1
|
---|
| 79 |
|
---|
[11225] | 80 | // visit nodes in order of decreasing height to ensure correct mapping
|
---|
[11229] | 81 | foreach (var v in n1.IterateNodesPrefix().OrderByDescending(x => compactedGraph[x].Depth)) {
|
---|
[11225] | 82 | if (forwardMap.ContainsKey(v))
|
---|
| 83 | continue;
|
---|
[11219] | 84 | var kv = compactedGraph[v];
|
---|
| 85 | ISymbolicExpressionTreeNode w = null;
|
---|
[11225] | 86 | foreach (var t in n2.IterateNodesPrefix()) {
|
---|
| 87 | if (reverseMap.ContainsKey(t) || compactedGraph[t] != kv)
|
---|
| 88 | continue;
|
---|
[11219] | 89 | w = t;
|
---|
| 90 | break;
|
---|
| 91 | }
|
---|
| 92 | if (w == null) continue;
|
---|
| 93 |
|
---|
| 94 | // at this point we know that v and w are isomorphic, however, the mapping cannot be done directly (as in the paper) because the trees are unordered (subtree order might differ)
|
---|
| 95 | // the solution is to sort subtrees by label using IterateBreadthOrdered (this will work because the subtrees are isomorphic!) and simultaneously iterate over the two subtrees
|
---|
[11486] | 96 | var eV = IterateBreadthOrdered(v, comparer).GetEnumerator();
|
---|
| 97 | var eW = IterateBreadthOrdered(w, comparer).GetEnumerator();
|
---|
[11219] | 98 |
|
---|
| 99 | while (eV.MoveNext() && eW.MoveNext()) {
|
---|
| 100 | var s = eV.Current;
|
---|
| 101 | var t = eW.Current;
|
---|
[11225] | 102 |
|
---|
[11486] | 103 | Debug.Assert(!reverseMap.ContainsKey(t));
|
---|
[11225] | 104 |
|
---|
[11219] | 105 | forwardMap[s] = t;
|
---|
| 106 | reverseMap[t] = s;
|
---|
| 107 | }
|
---|
| 108 | }
|
---|
| 109 |
|
---|
| 110 | return forwardMap;
|
---|
| 111 | }
|
---|
| 112 |
|
---|
| 113 | /// <summary>
|
---|
| 114 | /// Creates a compact representation of the two trees as a directed acyclic graph
|
---|
| 115 | /// </summary>
|
---|
[11229] | 116 | /// <param name="n1">The root of the first tree</param>
|
---|
| 117 | /// <param name="n2">The root of the second tree</param>
|
---|
[11219] | 118 | /// <returns>The compacted DAG representing the two trees</returns>
|
---|
[11229] | 119 | private Dictionary<ISymbolicExpressionTreeNode, GraphNode> Compact(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
|
---|
| 120 | var nodeMap = new Dictionary<ISymbolicExpressionTreeNode, GraphNode>(); // K
|
---|
| 121 | var labelMap = new Dictionary<string, GraphNode>(); // L
|
---|
[11219] | 122 | var childrenCount = new Dictionary<ISymbolicExpressionTreeNode, int>(); // Children
|
---|
| 123 |
|
---|
[11221] | 124 | var nodes = n1.IterateNodesPostfix().Concat(n2.IterateNodesPostfix()); // the disjoint union F
|
---|
[11486] | 125 | var list = new List<GraphNode>(); // preallocate size to avoid list resizing as it has a performance hit
|
---|
[11219] | 126 | var queue = new Queue<ISymbolicExpressionTreeNode>();
|
---|
| 127 |
|
---|
| 128 | foreach (var n in nodes) {
|
---|
| 129 | if (n.SubtreeCount == 0) {
|
---|
[11486] | 130 | var label = Label(n);
|
---|
[11229] | 131 | if (!labelMap.ContainsKey(label)) {
|
---|
| 132 | var z = new GraphNode { SymbolicExpressionTreeNode = n, Label = label };
|
---|
| 133 | labelMap[z.Label] = z;
|
---|
| 134 | list.Add(z);
|
---|
[11225] | 135 | }
|
---|
[11229] | 136 | nodeMap[n] = labelMap[label];
|
---|
[11219] | 137 | queue.Enqueue(n);
|
---|
| 138 | } else {
|
---|
| 139 | childrenCount[n] = n.SubtreeCount;
|
---|
| 140 | }
|
---|
| 141 | }
|
---|
| 142 | while (queue.Any()) {
|
---|
[11229] | 143 | var n = queue.Dequeue();
|
---|
[11225] | 144 |
|
---|
[11229] | 145 | if (n.SubtreeCount > 0) {
|
---|
| 146 | var label = n.Symbol.Name;
|
---|
[11219] | 147 | bool found = false;
|
---|
[11229] | 148 | var depth = n.GetDepth();
|
---|
[11219] | 149 |
|
---|
| 150 | bool sort = commutativeSymbols.Contains(label);
|
---|
[11229] | 151 | var nNodes = n.Subtrees.Select(x => nodeMap[x]).ToList();
|
---|
[11486] | 152 | if (sort) nNodes.Sort((a, b) => string.CompareOrdinal(a.Label, b.Label));
|
---|
[11219] | 153 |
|
---|
[11229] | 154 | for (int i = list.Count - 1; i >= 0; --i) {
|
---|
| 155 | var w = list[i];
|
---|
| 156 |
|
---|
| 157 | if (!(n.SubtreeCount == w.ChildrenCount && label == w.Label && depth == w.Depth))
|
---|
[11219] | 158 | continue;
|
---|
| 159 |
|
---|
| 160 | // sort V and W when the symbol is commutative because we are dealing with unordered trees
|
---|
[11229] | 161 | var m = w.SymbolicExpressionTreeNode;
|
---|
| 162 | var mNodes = m.Subtrees.Select(x => nodeMap[x]).ToList();
|
---|
[11486] | 163 | if (sort) mNodes.Sort((a, b) => string.CompareOrdinal(a.Label, b.Label));
|
---|
[11219] | 164 |
|
---|
[11229] | 165 | if (nNodes.SequenceEqual(mNodes)) {
|
---|
| 166 | nodeMap[n] = w;
|
---|
[11219] | 167 | found = true;
|
---|
| 168 | break;
|
---|
| 169 | }
|
---|
[11229] | 170 | }
|
---|
[11219] | 171 |
|
---|
| 172 | if (!found) {
|
---|
[11486] | 173 | var w = new GraphNode { SymbolicExpressionTreeNode = n, Label = label, Depth = depth };
|
---|
[11229] | 174 | list.Add(w);
|
---|
| 175 | nodeMap[n] = w;
|
---|
| 176 | }
|
---|
| 177 | }
|
---|
[11219] | 178 |
|
---|
[11486] | 179 | if (n == n1 || n == n2)
|
---|
| 180 | continue;
|
---|
| 181 |
|
---|
[11229] | 182 | var p = n.Parent;
|
---|
[11219] | 183 | if (p == null)
|
---|
| 184 | continue;
|
---|
| 185 |
|
---|
| 186 | childrenCount[p]--;
|
---|
| 187 |
|
---|
| 188 | if (childrenCount[p] == 0)
|
---|
| 189 | queue.Enqueue(p);
|
---|
[11220] | 190 | }
|
---|
[11219] | 191 |
|
---|
[11229] | 192 | return nodeMap;
|
---|
[11219] | 193 | }
|
---|
| 194 |
|
---|
[11486] | 195 | private IEnumerable<ISymbolicExpressionTreeNode> IterateBreadthOrdered(ISymbolicExpressionTreeNode node, ISymbolicExpressionTreeNodeComparer comparer) {
|
---|
[11219] | 196 | var list = new List<ISymbolicExpressionTreeNode> { node };
|
---|
| 197 | int i = 0;
|
---|
| 198 | while (i < list.Count) {
|
---|
| 199 | var n = list[i];
|
---|
| 200 | if (n.SubtreeCount > 0) {
|
---|
[11486] | 201 | var subtrees = commutativeSymbols.Contains(node.Symbol.Name) ? n.Subtrees.OrderBy(x => x, comparer) : n.Subtrees;
|
---|
[11219] | 202 | list.AddRange(subtrees);
|
---|
| 203 | }
|
---|
| 204 | i++;
|
---|
| 205 | }
|
---|
| 206 | return list;
|
---|
| 207 | }
|
---|
| 208 |
|
---|
[11486] | 209 | private string Label(ISymbolicExpressionTreeNode node) {
|
---|
| 210 | if (node.SubtreeCount > 0)
|
---|
| 211 | return node.Symbol.Name;
|
---|
| 212 |
|
---|
| 213 | var constant = node as ConstantTreeNode;
|
---|
| 214 | if (constant != null)
|
---|
| 215 | return MatchConstantValues ? constant.Value.ToString(CultureInfo.InvariantCulture) : constant.Symbol.Name;
|
---|
| 216 | var variable = node as VariableTreeNode;
|
---|
| 217 | if (variable != null) {
|
---|
| 218 | return MatchVariableWeights ? variable.Weight + variable.VariableName : variable.VariableName;
|
---|
| 219 | }
|
---|
| 220 |
|
---|
| 221 | return node.ToString();
|
---|
| 222 | }
|
---|
| 223 |
|
---|
[11229] | 224 | private class GraphNode {
|
---|
| 225 | public ISymbolicExpressionTreeNode SymbolicExpressionTreeNode;
|
---|
| 226 | public string Label;
|
---|
| 227 | public int Depth;
|
---|
[11486] | 228 | public int ChildrenCount { get { return SymbolicExpressionTreeNode.SubtreeCount; } }
|
---|
[11219] | 229 | }
|
---|
| 230 | }
|
---|
| 231 | }
|
---|