source: branches/2780_SAPBA/HeuristicLab.Algorithms.SAPBA/Strategies/LamarckianStrategy.cs @ 16386

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Algorithms.EGO;
using HeuristicLab.Algorithms.SAPBA.Operators;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.SAPBA {
  [StorableClass]
  public class LamarckianStrategy : InfillStrategy {
    #region Parameternames
    public const string NoTrainingPointsParameterName = "Number of Trainingpoints";
    public const string LocalInfillCriterionParameterName = "LocalInfillCriterion";
    public const string OptimizationAlgorithmParameterName = "Optimization Algorithm";
    public const string RegressionAlgorithmParameterName = "Regression Algorithm";
    #endregion
    #region Parameters
    public IFixedValueParameter<IntValue> NoTrainingPointsParameter => Parameters[NoTrainingPointsParameterName] as IFixedValueParameter<IntValue>;
    public IValueParameter<IAlgorithm> OptimizationAlgorithmParameter => Parameters[OptimizationAlgorithmParameterName] as IValueParameter<IAlgorithm>;
    public IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>> RegressionAlgorithmParameter => Parameters[RegressionAlgorithmParameterName] as IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>>;
    public IConstrainedValueParameter<IInfillCriterion> LocalInfillCriterionParameter => Parameters[LocalInfillCriterionParameterName] as IConstrainedValueParameter<IInfillCriterion>;
    #endregion
    #region Properties
    public IntValue NoTrainingPoints => NoTrainingPointsParameter.Value;
    public IAlgorithm OptimizationAlgorithm => OptimizationAlgorithmParameter.Value;
    public IDataAnalysisAlgorithm<IRegressionProblem> RegressionAlgorithm => RegressionAlgorithmParameter.Value;
    public IInfillCriterion LocalInfillCriterion => LocalInfillCriterionParameter.Value;
    #endregion

    #region Constructors
    [StorableConstructor]
    protected LamarckianStrategy(bool deserializing) : base(deserializing) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      RegisterParameterEvents();
    }
    protected LamarckianStrategy(LamarckianStrategy original, Cloner cloner) : base(original, cloner) {
      RegisterParameterEvents();
    }
    public LamarckianStrategy() {
      var localCritera = new ItemSet<IInfillCriterion> { new ExpectedQuality(), new ExpectedImprovement(), new AugmentedExpectedImprovement(), new ExpectedQuantileImprovement(), new MinimalQuantileCriterium(), new PluginExpectedImprovement() };
      var osEs = new OffspringSelectionEvolutionStrategy.OffspringSelectionEvolutionStrategy {
        Problem = new InfillProblem(),
        ComparisonFactor = { Value = 1.0 },
        MaximumGenerations = { Value = 1000 },
        MaximumEvaluatedSolutions = { Value = 100000 },
        PlusSelection = { Value = true },
        PopulationSize = { Value = 1 }
      };
      osEs.MutatorParameter.Value = osEs.MutatorParameter.ValidValues.OfType<MultiRealVectorManipulator>().First();
      Parameters.Add(new FixedValueParameter<IntValue>(NoTrainingPointsParameterName, "The number of sample points used to create a local model", new IntValue(50)));
      Parameters.Add(new ConstrainedValueParameter<IInfillCriterion>(LocalInfillCriterionParameterName, "The infill criterion used to cheaply evaluate points.", localCritera, localCritera.First()));
      Parameters.Add(new ValueParameter<IAlgorithm>(OptimizationAlgorithmParameterName, "The algorithm used to solve the expected improvement subproblem", osEs));
      Parameters.Add(new ValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>>(RegressionAlgorithmParameterName, "The model used to approximate the problem", new GaussianProcessRegression { Problem = new RegressionProblem() }));
      RegisterParameterEvents();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new LamarckianStrategy(this, cloner);
    }
    #endregion

    //Short lived stores for analysis
    private readonly List<double> LamarckValues = new List<double>();
    private readonly List<double> SampleValues = new List<double>();

    protected override void Initialize() {
      base.Initialize();
      var infillProblem = OptimizationAlgorithm.Problem as InfillProblem;
      if (infillProblem == null) throw new ArgumentException("InfillOptimizationAlgorithm does not have an InfillProblem.");
      infillProblem.InfillCriterion = LocalInfillCriterion;
    }
    protected override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, ResultCollection globalResults, IRandom random) {
      base.Analyze(individuals, qualities, results, globalResults, random);
      const string plotName = "Lamarck Comparison";
      const string lamarckRow = "Lamarck Values";
      const string samplesRow = "Original Values";
      if (!globalResults.ContainsKey(plotName))
        globalResults.Add(new Result(plotName, new DataTable(plotName)));

      var plot = (DataTable)globalResults[plotName].Value;
      if (!plot.Rows.ContainsKey(lamarckRow)) plot.Rows.Add(new DataRow(lamarckRow));
      if (!plot.Rows.ContainsKey(samplesRow)) plot.Rows.Add(new DataRow(samplesRow));
      plot.Rows[lamarckRow].Values.AddRange(LamarckValues);
      plot.Rows[samplesRow].Values.AddRange(SampleValues);
      LamarckValues.Clear();
      SampleValues.Clear();

      //analyze Hypervolumes 
      const string volPlotName = "Hypervolumes Comparison";
      const string mainRowName = "Population Volume (log)";
      const string subspaceRowName = "Subspace Volume (log) for Lamarck Candidate ";
      if (!globalResults.ContainsKey(volPlotName))
        globalResults.Add(new Result(volPlotName, new DataTable(volPlotName)));

      plot = (DataTable)globalResults[volPlotName].Value;
      if (!plot.Rows.ContainsKey(mainRowName)) plot.Rows.Add(new DataRow(mainRowName));
      var v = Math.Log(GetStableVolume(GetBoundingBox(individuals.Select(x => x.RealVector()))));
      plot.Rows[mainRowName].Values.Add(v);

      var indis = individuals
          .Zip(qualities, (individual, d) => new Tuple<Individual, double>(individual, d))
          .OrderBy(t => SapbaAlgorithm.Problem.Maximization ? -t.Item2 : t.Item2)
          .Take(NoIndividuals.Value)
          .Select(t => t.Item1).ToArray();

      for (var i = 0; i < indis.Length; i++) {
        var samples = GetNearestSamples(NoTrainingPoints.Value, indis[i].RealVector());
        var d = Math.Log(GetStableVolume(GetBoundingBox(samples.Select(x => x.Item1))));
        if (!plot.Rows.ContainsKey(subspaceRowName + i)) plot.Rows.Add(new DataRow(subspaceRowName + i));
        plot.Rows[subspaceRowName + i].Values.Add(d);
      }



    }
    protected override void ProcessPopulation(Individual[] individuals, double[] qualities, IRandom random) {
      if (RegressionSolution == null) return;
      if (Generations < NoGenerations.Value) Generations++;
      else {
        //Select best Individuals
        var indis = individuals
          .Zip(qualities, (individual, d) => new Tuple<Individual, double>(individual, d))
          .OrderBy(t => Problem.Maximization ? -t.Item2 : t.Item2)
          .Take(NoIndividuals.Value)
          .Select(t => t.Item1).ToArray();
        //Evaluate individuals
        foreach (var individual in indis)
          SampleValues.Add(EvaluateSample(individual.RealVector(), random).Item2);

        //Perform memetic replacement for all points
        for (var i = 0; i < indis.Length; i++) {
          var vector = indis[i].RealVector();
          var altVector = OptimizeInfillProblem(vector, random);
          LamarckValues.Add(EvaluateSample(altVector, random).Item2);
          if (LamarckValues[i] < SampleValues[i] == Problem.Maximization) continue;
          for (var j = 0; j < vector.Length; j++) vector[j] = altVector[j];
        }

        BuildRegressionSolution(random);
        Generations = 0;
      }
    }

    #region Events
    private void RegisterParameterEvents() {
      OptimizationAlgorithmParameter.ValueChanged += OnInfillAlgorithmChanged;
      OptimizationAlgorithm.ProblemChanged += OnInfillProblemChanged;
      LocalInfillCriterionParameter.ValueChanged += OnInfillCriterionChanged;
    }
    private void OnInfillCriterionChanged(object sender, EventArgs e) {
      ((InfillProblem)OptimizationAlgorithm.Problem).InfillCriterion = LocalInfillCriterion;
    }
    private void OnInfillAlgorithmChanged(object sender, EventArgs e) {
      OptimizationAlgorithm.Problem = new InfillProblem { InfillCriterion = LocalInfillCriterion };
      OptimizationAlgorithm.ProblemChanged -= OnInfillProblemChanged; //avoid double attaching
      OptimizationAlgorithm.ProblemChanged += OnInfillProblemChanged;
    }
    private void OnInfillProblemChanged(object sender, EventArgs e) {
      OptimizationAlgorithm.ProblemChanged -= OnInfillProblemChanged;
      OptimizationAlgorithm.Problem = new InfillProblem { InfillCriterion = LocalInfillCriterion };
      OptimizationAlgorithm.ProblemChanged += OnInfillProblemChanged;
    }
    #endregion

    #region helpers
    private RealVector OptimizeInfillProblem(RealVector point, IRandom random) {
      var infillProblem = OptimizationAlgorithm.Problem as InfillProblem;
      if (infillProblem == null) throw new ArgumentException("InfillOptimizationAlgorithm does not have an InfillProblem.");
      if (infillProblem.InfillCriterion != LocalInfillCriterion) throw new ArgumentException("InfillCiriterion for Problem is not correctly set.");

      var points = Math.Min(NoTrainingPoints.Value, Samples.Count);
      var samples = GetNearestSamples(points, point);
      var regression = SapbaUtilities.BuildModel(samples, RegressionAlgorithm, random);
      var box = GetBoundingBox(samples.Select(x => x.Item1));

      infillProblem.Encoding.Length = ((RealVectorEncoding)Problem.Encoding).Length;
      infillProblem.Encoding.Bounds = box;
      infillProblem.Encoding.SolutionCreator = new FixedRealVectorCreator(point);
      infillProblem.Initialize(regression, Problem.Maximization);
      var res = SapbaUtilities.SyncRunSubAlgorithm(OptimizationAlgorithm, random.Next(int.MaxValue));
      if (!res.ContainsKey(InfillProblem.BestInfillSolutionResultName)) throw new ArgumentException("The InfillOptimizationAlgorithm did not return a best solution");
      var v = res[InfillProblem.BestInfillSolutionResultName].Value as RealVector;
      if (v == null) throw new ArgumentException("The InfillOptimizationAlgorithm did not return the expected result types");
      if (!InBounds(v, box)) throw new ArgumentException("Vector not in bounds");
      OptimizationAlgorithm.Runs.Clear();
      return v;
    }
    private Tuple<RealVector, double>[] GetNearestSamples(int noSamples, RealVector point) {
      return Samples.Select(sample => Tuple.Create(SquaredEuclidean(sample.Item1, point), sample)).OrderBy(x => x.Item1).Take(noSamples).Select(x => x.Item2).ToArray();
    }
    private static DoubleMatrix GetBoundingBox(IEnumerable<RealVector> samples) {
      DoubleMatrix m = null;
      foreach (var sample in samples)
        if (m == null) {
          m = new DoubleMatrix(sample.Length, 2);
          for (var i = 0; i < sample.Length; i++) m[i, 0] = m[i, 1] = sample[i];
        } else
          for (var i = 0; i < sample.Length; i++) {
            m[i, 0] = Math.Min(m[i, 0], sample[i]);
            m[i, 1] = Math.Max(m[i, 1], sample[i]);
          }
      return m;
    }

    //the volume of a bounded-box whith slightly increased dimensions (Volume can never reach 0)
    private static double GetStableVolume(DoubleMatrix bounds) {
      var res = 1.0;
      for (var i = 0; i < bounds.Rows; i++) res *= bounds[i, 1] - bounds[i, 0] + 0.1;
      return res;
    }
    private static bool InBounds(RealVector r, DoubleMatrix bounds) {
      return !r.Where((t, i) => t < bounds[i, 0] || t > bounds[i, 1]).Any();
    }
    private static double SquaredEuclidean(RealVector a, RealVector b) {
      return a.Select((t, i) => t - b[i]).Sum(d => d * d);
    }
    #endregion
  }
}