#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
///
/// An operator that analyzes the training best symbolic data analysis solution for multi objective symbolic data analysis problems.
///
[Item("SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer", "An operator that analyzes the training best symbolic data analysis solution for multi objective symbolic data analysis problems.")]
[StorableType("E57C5746-B9AF-41E1-8B0C-9AA519A5C9B5")]
public abstract class SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveAnalyzer
where T : class, ISymbolicDataAnalysisSolution {
private const string TrainingBestSolutionsParameterName = "Best training solutions";
private const string TrainingBestSolutionQualitiesParameterName = "Best training solution qualities";
private const string UpdateAlwaysParameterName = "Always update best solutions";
private const string TrainingBestSolutionParameterName = "Best training solution";
#region parameter properties
public ILookupParameter> TrainingBestSolutionsParameter {
get { return (ILookupParameter>)Parameters[TrainingBestSolutionsParameterName]; }
}
public ILookupParameter> TrainingBestSolutionQualitiesParameter {
get { return (ILookupParameter>)Parameters[TrainingBestSolutionQualitiesParameterName]; }
}
public IFixedValueParameter UpdateAlwaysParameter {
get { return (IFixedValueParameter)Parameters[UpdateAlwaysParameterName]; }
}
#endregion
#region properties
private ItemList TrainingBestSolutions {
get { return TrainingBestSolutionsParameter.ActualValue; }
set { TrainingBestSolutionsParameter.ActualValue = value; }
}
private ItemList TrainingBestSolutionQualities {
get { return TrainingBestSolutionQualitiesParameter.ActualValue; }
set { TrainingBestSolutionQualitiesParameter.ActualValue = value; }
}
public bool UpdateAlways {
get { return UpdateAlwaysParameter.Value.Value; }
set { UpdateAlwaysParameter.Value.Value = value; }
}
#endregion
[StorableConstructor]
protected SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
protected SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer(SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { }
public SymbolicDataAnalysisMultiObjectiveTrainingBestSolutionAnalyzer()
: base() {
Parameters.Add(new LookupParameter>(TrainingBestSolutionsParameterName, "The training best (Pareto-optimal) symbolic data analysis solutions."));
Parameters.Add(new LookupParameter>(TrainingBestSolutionQualitiesParameterName, "The qualities of the training best (Pareto-optimal) solutions."));
Parameters.Add(new FixedValueParameter(UpdateAlwaysParameterName, "Determines if the best training solutions should always be updated regardless of its quality.", new BoolValue(false)));
UpdateAlwaysParameter.Hidden = true;
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
if (!Parameters.ContainsKey(UpdateAlwaysParameterName)) {
Parameters.Add(new FixedValueParameter(UpdateAlwaysParameterName, "Determines if the best training solutions should always be updated regardless of its quality.", new BoolValue(false)));
UpdateAlwaysParameter.Hidden = true;
}
}
public override IOperation Apply() {
var results = ResultCollection;
// create empty parameter and result values
if (TrainingBestSolutions == null) {
TrainingBestSolutions = new ItemList();
TrainingBestSolutionQualities = new ItemList();
results.Add(new Result(TrainingBestSolutionQualitiesParameter.Name, TrainingBestSolutionQualitiesParameter.Description, TrainingBestSolutionQualities));
results.Add(new Result(TrainingBestSolutionsParameter.Name, TrainingBestSolutionsParameter.Description, TrainingBestSolutions));
}
if (!results.ContainsKey(TrainingBestSolutionParameterName)) {
results.Add(new Result(TrainingBestSolutionParameterName, "", typeof(ISymbolicDataAnalysisSolution)));
}
//if the pareto front of best solutions shall be updated regardless of the quality, the list initialized empty to discard old solutions
List trainingBestQualities;
if (UpdateAlways) {
trainingBestQualities = new List();
} else {
trainingBestQualities = TrainingBestSolutionQualities.Select(x => x.ToArray()).ToList();
}
ISymbolicExpressionTree[] trees = SymbolicExpressionTree.ToArray();
List qualities = Qualities.Select(x => x.ToArray()).ToList();
bool[] maximization = Maximization.ToArray();
var nonDominatedIndividuals = new[] { new { Tree = default(ISymbolicExpressionTree), Qualities = default(double[]) } }.ToList();
nonDominatedIndividuals.Clear();
// build list of new non-dominated solutions
for (int i = 0; i < trees.Length; i++) {
if (IsNonDominated(qualities[i], nonDominatedIndividuals.Select(ind => ind.Qualities), maximization) &&
IsNonDominated(qualities[i], trainingBestQualities, maximization)) {
for (int j = nonDominatedIndividuals.Count - 1; j >= 0; j--) {
if (IsBetterOrEqual(qualities[i], nonDominatedIndividuals[j].Qualities, maximization)) {
nonDominatedIndividuals.RemoveAt(j);
}
}
nonDominatedIndividuals.Add(new { Tree = trees[i], Qualities = qualities[i] });
}
}
var nonDominatedSolutions = nonDominatedIndividuals.Select(x => new { Solution = CreateSolution(x.Tree, x.Qualities), Qualities = x.Qualities }).ToList();
nonDominatedSolutions.ForEach(s => s.Solution.Name = string.Join(",", s.Qualities.Select(q => q.ToString())));
#region update Pareto-optimal solution archive
if (nonDominatedSolutions.Count > 0) {
//add old non-dominated solutions only if they are not dominated by one of the new solutions
for (int i = 0; i < trainingBestQualities.Count; i++) {
if (IsNonDominated(trainingBestQualities[i], nonDominatedSolutions.Select(x => x.Qualities), maximization)) {
nonDominatedSolutions.Add(new { Solution = TrainingBestSolutions[i], Qualities = TrainingBestSolutionQualities[i].ToArray() });
}
}
//assumes the the first objective is always the accuracy
var sortedNonDominatedSolutions = maximization[0]
? nonDominatedSolutions.OrderByDescending(x => x.Qualities[0])
: nonDominatedSolutions.OrderBy(x => x.Qualities[0]);
var trainingBestSolution = sortedNonDominatedSolutions.Select(s => s.Solution).First();
results[TrainingBestSolutionParameterName].Value = trainingBestSolution;
TrainingBestSolutions = new ItemList(sortedNonDominatedSolutions.Select(x => x.Solution));
results[TrainingBestSolutionsParameter.Name].Value = TrainingBestSolutions;
TrainingBestSolutionQualities = new ItemList(sortedNonDominatedSolutions.Select(x => new DoubleArray(x.Qualities)));
results[TrainingBestSolutionQualitiesParameter.Name].Value = TrainingBestSolutionQualities;
}
#endregion
return base.Apply();
}
protected abstract T CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality);
private bool IsNonDominated(double[] point, IEnumerable points, bool[] maximization) {
foreach (var refPoint in points) {
bool refPointDominatesPoint = IsBetterOrEqual(refPoint, point, maximization);
if (refPointDominatesPoint) return false;
}
return true;
}
private bool IsBetterOrEqual(double[] lhs, double[] rhs, bool[] maximization) {
for (int i = 0; i < lhs.Length; i++) {
var result = IsBetterOrEqual(lhs[i], rhs[i], maximization[i]);
if (!result) return false;
}
return true;
}
private bool IsBetterOrEqual(double lhs, double rhs, bool maximization) {
if (maximization) return lhs >= rhs;
else return lhs <= rhs;
}
}
}