#region License Information
/* HeuristicLab
* Copyright (C) 2002-2019 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.Globalization;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization.Operators;
using HEAL.Attic;
using NodeMap = System.Collections.Generic.Dictionary;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
[StorableType("63ACB7A4-137F-468F-BE42-A4CA6B62C63B")]
[Item("SymbolicExpressionTreeBottomUpSimilarityCalculator", "A similarity calculator which uses the tree bottom-up distance as a similarity metric.")]
public class SymbolicExpressionTreeBottomUpSimilarityCalculator : SolutionSimilarityCalculator {
private readonly HashSet commutativeSymbols = new HashSet { "Addition", "Multiplication", "Average", "And", "Or", "Xor" };
public SymbolicExpressionTreeBottomUpSimilarityCalculator() { }
protected override bool IsCommutative { get { return true; } }
public bool MatchConstantValues { get; set; }
public bool MatchVariableWeights { get; set; }
[StorableConstructor]
protected SymbolicExpressionTreeBottomUpSimilarityCalculator(StorableConstructorFlag _) : base(_) {
}
protected SymbolicExpressionTreeBottomUpSimilarityCalculator(SymbolicExpressionTreeBottomUpSimilarityCalculator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicExpressionTreeBottomUpSimilarityCalculator(this, cloner);
}
#region static methods
private static ISymbolicExpressionTreeNode ActualRoot(ISymbolicExpressionTree tree) {
return tree.Root.GetSubtree(0).GetSubtree(0);
}
public static double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2, bool strict = false) {
return CalculateSimilarity(ActualRoot(t1), ActualRoot(t2), strict);
}
public static double CalculateSimilarity(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2, bool strict = false) {
var calculator = new SymbolicExpressionTreeBottomUpSimilarityCalculator { MatchConstantValues = strict, MatchVariableWeights = strict };
return CalculateSimilarity(n1, n2, strict);
}
public static Dictionary ComputeBottomUpMapping(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2, bool strict = false) {
return ComputeBottomUpMapping(ActualRoot(t1), ActualRoot(t2), strict);
}
public static Dictionary ComputeBottomUpMapping(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2, bool strict = false) {
var calculator = new SymbolicExpressionTreeBottomUpSimilarityCalculator { MatchConstantValues = strict, MatchVariableWeights = strict };
return calculator.ComputeBottomUpMapping(n1, n2);
}
#endregion
public double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) {
return CalculateSimilarity(t1, t2, out Dictionary map);
}
public double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2, out NodeMap map) {
if (t1 == t2) {
map = null;
return 1;
}
map = ComputeBottomUpMapping(t1, t2);
return 2.0 * map.Count / (t1.Length + t2.Length - 4); // -4 for skipping root and start symbols in the two trees
}
public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) {
if (leftSolution == rightSolution)
return 1.0;
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(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) {
return ComputeBottomUpMapping(t1.Root.GetSubtree(0).GetSubtree(0), t2.Root.GetSubtree(0).GetSubtree(0));
}
public Dictionary ComputeBottomUpMapping(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
var compactedGraph = Compact(n1, n2);
IEnumerable Subtrees(ISymbolicExpressionTreeNode node, bool commutative) {
var subtrees = node.IterateNodesPrefix();
return commutative ? subtrees.OrderBy(x => compactedGraph[x].Hash) : subtrees;
}
var nodes1 = n1.IterateNodesPostfix().OrderByDescending(x => x.GetLength()); // by descending length so that largest subtrees are mapped first
var nodes2 = (List)n2.IterateNodesPostfix();
var forward = new NodeMap();
var reverse = new NodeMap();
foreach (ISymbolicExpressionTreeNode v in nodes1) {
if (forward.ContainsKey(v))
continue;
var kv = compactedGraph[v];
var commutative = v.SubtreeCount > 1 && commutativeSymbols.Contains(kv.Label);
foreach (ISymbolicExpressionTreeNode w in nodes2) {
if (w.GetLength() != kv.Length || w.GetDepth() != kv.Depth || reverse.ContainsKey(w) || compactedGraph[w] != kv)
continue;
// map one whole subtree to the other
foreach (var t in Subtrees(v, commutative).Zip(Subtrees(w, commutative), Tuple.Create)) {
forward[t.Item1] = t.Item2;
reverse[t.Item2] = t.Item1;
}
break;
}
}
return forward;
}
///
/// 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 nodes = n1.IterateNodesPostfix().Concat(n2.IterateNodesPostfix()); // the disjoint union F
var graph = new List();
IEnumerable Subtrees(GraphNode g, bool commutative) {
var subtrees = g.SymbolicExpressionTreeNode.Subtrees.Select(x => nodeMap[x]);
return commutative ? subtrees.OrderBy(x => x.Hash) : subtrees;
}
foreach (var node in nodes) {
var label = GetLabel(node);
if (node.SubtreeCount == 0) {
if (!labelMap.ContainsKey(label)) {
labelMap[label] = new GraphNode(node, label);
}
nodeMap[node] = labelMap[label];
} else {
var v = new GraphNode(node, label);
bool found = false;
var commutative = node.SubtreeCount > 1 && commutativeSymbols.Contains(label);
var vv = Subtrees(v, commutative);
foreach (var w in graph) {
if (v.Depth != w.Depth || v.SubtreeCount != w.SubtreeCount || v.Length != w.Length || v.Label != w.Label) {
continue;
}
var ww = Subtrees(w, commutative);
found = vv.SequenceEqual(ww);
if (found) {
nodeMap[node] = w;
break;
}
}
if (!found) {
nodeMap[node] = v;
graph.Add(v);
}
}
}
return nodeMap;
}
private string GetLabel(ISymbolicExpressionTreeNode node) {
if (node.SubtreeCount > 0)
return node.Symbol.Name;
if (node is ConstantTreeNode constant)
return MatchConstantValues ? constant.Value.ToString(CultureInfo.InvariantCulture) : constant.Symbol.Name;
if (node is VariableTreeNode variable)
return MatchVariableWeights ? variable.Weight + variable.VariableName : variable.VariableName;
return node.ToString();
}
private class GraphNode {
private GraphNode() { }
public GraphNode(ISymbolicExpressionTreeNode node, string label) {
SymbolicExpressionTreeNode = node;
Label = label;
Hash = GetHashCode();
Depth = node.GetDepth();
Length = node.GetLength();
}
public int Hash { get; }
public ISymbolicExpressionTreeNode SymbolicExpressionTreeNode { get; }
public string Label { get; }
public int Depth { get; }
public int SubtreeCount { get { return SymbolicExpressionTreeNode.SubtreeCount; } }
public int Length { get; }
}
}
}