#region License Information /* HeuristicLab * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Diagnostics; using System.Globalization; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Optimization.Operators; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic { [StorableClass] [Item("SymbolicExpressionTreeBottomUpSimilarityCalculator", "A similarity calculator which uses the tree bottom-up distance as a similarity metric.")] public class SymbolicExpressionTreeBottomUpSimilarityCalculator : SingleObjectiveSolutionSimilarityCalculator { private readonly HashSet commutativeSymbols = new HashSet { "Addition", "Multiplication", "Average", "And", "Or", "Xor" }; public bool MatchVariableWeights { get; set; } public bool MatchConstantValues { get; set; } public SymbolicExpressionTreeBottomUpSimilarityCalculator() { } [StorableConstructor] protected SymbolicExpressionTreeBottomUpSimilarityCalculator(bool deserializing) : base(deserializing) { } protected SymbolicExpressionTreeBottomUpSimilarityCalculator(SymbolicExpressionTreeBottomUpSimilarityCalculator original, Cloner cloner) : base(original, cloner) { MatchVariableWeights = original.MatchVariableWeights; MatchConstantValues = original.MatchConstantValues; } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicExpressionTreeBottomUpSimilarityCalculator(this, cloner); } public double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) { if (t1 == t2) return 1; var map = ComputeBottomUpMapping(t1.Root, t2.Root); return 2.0 * map.Count / (t1.Length + t2.Length); } public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) { var t1 = leftSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree; var t2 = rightSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree; if (t1 == null || t2 == null) throw new ArgumentException("Cannot calculate similarity when one of the arguments is null."); var similarity = CalculateSimilarity(t1, t2); if (similarity > 1.0) throw new Exception("Similarity value cannot be greater than 1"); return similarity; } public Dictionary ComputeBottomUpMapping(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) { var comparer = new SymbolicExpressionTreeNodeComparer(); // use a node comparer because it's faster than calling node.ToString() (strings are expensive) and comparing strings var compactedGraph = Compact(n1, n2); var forwardMap = new Dictionary(); // nodes of t1 => nodes of t2 var reverseMap = new Dictionary(); // nodes of t2 => nodes of t1 // visit nodes in order of decreasing height to ensure correct mapping var nodes1 = n1.IterateNodesPrefix().ToList(); var nodes2 = n2.IterateNodesPrefix().ToList(); for (int i = 0; i < nodes1.Count; ++i) { var v = nodes1[i]; if (forwardMap.ContainsKey(v)) continue; var kv = compactedGraph[v]; ISymbolicExpressionTreeNode w = null; for (int j = 0; j < nodes2.Count; ++j) { var t = nodes2[j]; if (reverseMap.ContainsKey(t) || compactedGraph[t] != kv) continue; w = t; break; } if (w == null) continue; // 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) // 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 var vv = IterateBreadthOrdered(v, comparer).ToList(); var ww = IterateBreadthOrdered(w, comparer).ToList(); int len = Math.Min(vv.Count, ww.Count); for (int j = 0; j < len; ++j) { var s = vv[j]; var t = ww[j]; Debug.Assert(!reverseMap.ContainsKey(t)); forwardMap[s] = t; reverseMap[t] = s; } } return forwardMap; } /// /// Creates a compact representation of the two trees as a directed acyclic graph /// /// The root of the first tree /// The root of the second tree /// The compacted DAG representing the two trees private Dictionary Compact(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) { var nodeMap = new Dictionary(); // K var labelMap = new Dictionary(); // L var childrenCount = new Dictionary(); // Children var nodes = n1.IterateNodesPostfix().Concat(n2.IterateNodesPostfix()); // the disjoint union F var list = new List(); var queue = new Queue(); foreach (var n in nodes) { if (n.SubtreeCount == 0) { var label = Label(n); if (!labelMap.ContainsKey(label)) { var z = new GraphNode { SymbolicExpressionTreeNode = n, Label = label }; labelMap[z.Label] = z; } nodeMap[n] = labelMap[label]; queue.Enqueue(n); } else { childrenCount[n] = n.SubtreeCount; } } while (queue.Any()) { var n = queue.Dequeue(); if (n.SubtreeCount > 0) { bool found = false; var label = n.Symbol.Name; var depth = n.GetDepth(); bool sort = n.SubtreeCount > 1 && commutativeSymbols.Contains(label); var nSubtrees = n.Subtrees.Select(x => nodeMap[x]).ToList(); if (sort) nSubtrees.Sort((a, b) => string.CompareOrdinal(a.Label, b.Label)); for (int i = list.Count - 1; i >= 0; --i) { var w = list[i]; if (!(n.SubtreeCount == w.SubtreeCount && label == w.Label && depth == w.Depth)) continue; // sort V and W when the symbol is commutative because we are dealing with unordered trees var m = w.SymbolicExpressionTreeNode; var mSubtrees = m.Subtrees.Select(x => nodeMap[x]).ToList(); if (sort) mSubtrees.Sort((a, b) => string.CompareOrdinal(a.Label, b.Label)); found = nSubtrees.SequenceEqual(mSubtrees); if (found) { nodeMap[n] = w; break; } } if (!found) { var w = new GraphNode { SymbolicExpressionTreeNode = n, Label = label, Depth = depth }; list.Add(w); nodeMap[n] = w; } } if (n == n1 || n == n2) continue; var p = n.Parent; if (p == null) continue; childrenCount[p]--; if (childrenCount[p] == 0) queue.Enqueue(p); } return nodeMap; } private IEnumerable IterateBreadthOrdered(ISymbolicExpressionTreeNode node, ISymbolicExpressionTreeNodeComparer comparer) { var list = new List { node }; int i = 0; while (i < list.Count) { var n = list[i]; if (n.SubtreeCount > 0) { var subtrees = commutativeSymbols.Contains(node.Symbol.Name) ? n.Subtrees.OrderBy(x => x, comparer) : n.Subtrees; list.AddRange(subtrees); } i++; } return list; } private string Label(ISymbolicExpressionTreeNode node) { if (node.SubtreeCount > 0) return node.Symbol.Name; var constant = node as ConstantTreeNode; if (constant != null) return MatchConstantValues ? constant.Value.ToString(CultureInfo.InvariantCulture) : constant.Symbol.Name; var variable = node as VariableTreeNode; if (variable != null) { return MatchVariableWeights ? variable.Weight + variable.VariableName : variable.VariableName; } return node.ToString(); } private class GraphNode { public ISymbolicExpressionTreeNode SymbolicExpressionTreeNode; public string Label; public int Depth; public int SubtreeCount { get { return SymbolicExpressionTreeNode.SubtreeCount; } } } } }