source: branches/2780_SAPBA/HeuristicLab.Algorithms.SAPBA/Strategies/StrategyBase.cs @ 17874

#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 System.Threading;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.SAPBA.Strategies {
  [StorableClass]
  public abstract class StrategyBase : ParameterizedNamedItem, ISurrogateStrategy {
    #region Properties
    [Storable]
    protected SurrogateAssistedPopulationBasedAlgorithm Algorithm;
    [Storable]
    private List<Tuple<RealVector, double>> Samples;
    [Storable]
    protected IRegressionSolution RegressionSolution;
    protected CancellationToken Cancellation;
    private IEnumerable<Tuple<RealVector, double>> TruncatedSamples => Samples.Count > Algorithm.MaximalDatasetSize && Algorithm.MaximalDatasetSize > 0 ? Samples.Skip(Samples.Count - Algorithm.MaximalDatasetSize) : Samples;
    #endregion

    #region ResultName
    private const string BestQualityResultName = "Best Quality";
    private const string BestSolutionResultName = "Best Solution";
    private const string QualityTableResultName = "Qualities";
    private const string BestQualityRowName = "Best Quality";
    private const string WorstQualityRowName = "Worst Quality";
    private const string CurrentQualityRowName = "Current Quality";
    private const string MedianQualityRowName = "Median Quality";
    private const string AverageQualityRowName = "Average Quality";
    private const string RegressionSolutionResultName = "Model";
    private const string EvaluatedSoultionsResultName = "EvaluatedSolutions";
    private const string IterationsResultName = "Iterations";
    #endregion

    #region constructors
    [StorableConstructor]
    protected StrategyBase(bool deserializing) : base(deserializing) { }
    protected StrategyBase(StrategyBase original, Cloner cloner) : base(original, cloner) {
      if (original.Samples != null) Samples = original.Samples.Select(x => new Tuple<RealVector, double>(cloner.Clone(x.Item1), x.Item2)).ToList();
      RegressionSolution = cloner.Clone(original.RegressionSolution);
    }
    protected StrategyBase() { }
    #endregion

    public abstract double Evaluate(RealVector r, IRandom random);
    protected abstract void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, ResultCollection globalResults, IRandom random);
    protected abstract void ProcessPopulation(Individual[] individuals, double[] qualities, IRandom random);
    protected abstract void Initialize();

    public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
      Algorithm.Problem.Analyze(individuals, qualities, results, random);
      ProcessPopulation(individuals, qualities, random);

      var globalResults = Algorithm.Results;
      if (!globalResults.ContainsKey(EvaluatedSoultionsResultName)) globalResults.Add(new Result(EvaluatedSoultionsResultName, new IntValue(Samples.Count)));
      else ((IntValue)globalResults[EvaluatedSoultionsResultName].Value).Value = Samples.Count;
      if (!globalResults.ContainsKey(IterationsResultName)) globalResults.Add(new Result(IterationsResultName, new IntValue(0)));
      else ((IntValue)globalResults[IterationsResultName].Value).Value++;

      if (Samples.Count != 0) {
        var min = Samples.Min(x => x.Item2);
        var max = Samples.Max(x => x.Item2);
        var bestIdx = Algorithm.Problem.Maximization ? Samples.ArgMax(x => x.Item2) : Samples.ArgMin(x => x.Item2);

        if (!globalResults.ContainsKey(BestQualityResultName)) globalResults.Add(new Result(BestQualityResultName, new DoubleValue(0.0)));
        ((DoubleValue)globalResults[BestQualityResultName].Value).Value = Samples[bestIdx].Item2;
        if (!globalResults.ContainsKey(BestSolutionResultName)) globalResults.Add(new Result(BestSolutionResultName, new RealVector()));
        globalResults[BestSolutionResultName].Value = Samples[bestIdx].Item1;

        DataTable table;
        if (!globalResults.ContainsKey(QualityTableResultName)) {
          table = new DataTable("Qualites", "Qualites over iteration");
          globalResults.Add(new Result(QualityTableResultName, table));
          table.Rows.Add(new DataRow(BestQualityRowName, "Best Quality"));
          table.Rows.Add(new DataRow(WorstQualityRowName, "Worst Quality"));
          table.Rows.Add(new DataRow(CurrentQualityRowName, "Current Quality"));
          table.Rows.Add(new DataRow(MedianQualityRowName, "Median Quality"));
          table.Rows.Add(new DataRow(AverageQualityRowName, "Average Quality"));
        }
        table = (DataTable)globalResults[QualityTableResultName].Value;
        table.Rows[BestQualityResultName].Values.Add(Algorithm.Problem.Maximization ? max : min);
        table.Rows[WorstQualityRowName].Values.Add(Algorithm.Problem.Maximization ? min : max);
        table.Rows[CurrentQualityRowName].Values.Add(Samples[Samples.Count - 1].Item2);
        table.Rows[AverageQualityRowName].Values.Add(Samples.Average(x => x.Item2));
        table.Rows[MedianQualityRowName].Values.Add(Samples.Select(x => x.Item2).Median());
      }

      if (RegressionSolution != null) {
        if (!globalResults.ContainsKey(RegressionSolutionResultName))
          globalResults.Add(new Result(RegressionSolutionResultName, RegressionSolution));
        else
          globalResults[RegressionSolutionResultName].Value = RegressionSolution;
      }

      Analyze(individuals, qualities, results, globalResults, random);
    }
    public void Initialize(SurrogateAssistedPopulationBasedAlgorithm algorithm) {
      Algorithm = algorithm;
      Samples = algorithm.InitialSamples?.ToList() ?? new List<Tuple<RealVector, double>>();
      RegressionSolution = null;
      Initialize();
    }

    #region Helpers for Subclasses
    protected void BuildRegressionSolution(IRandom random) {
      RegressionSolution = EgoUtilities.BuildModel(Cancellation, TruncatedSamples, Algorithm.RegressionAlgorithm, random, Algorithm.RemoveDuplicates, RegressionSolution);
    }
    protected Tuple<RealVector, double> EvaluateSample(RealVector point, IRandom random) {
      Cancellation.ThrowIfCancellationRequested();
      if (Samples.Count >= Algorithm.MaximumEvaluations) { Algorithm.OptimizationAlgorithm.Stop(); return new Tuple<RealVector, double>(point, 0.0); }
      var p = new Tuple<RealVector, double>(point, Algorithm.Problem.Evaluate(GetIndividual(point), random));
      Samples.Add(p);
      return p;
    }
    protected Tuple<RealVector, double> EstimateSample(RealVector point, IRandom random) {
      if (Samples.Count == Algorithm.InitialEvaluations && RegressionSolution == null) BuildRegressionSolution(random);
      return Samples.Count < Algorithm.InitialEvaluations ? EvaluateSample(point, random) : new Tuple<RealVector, double>(point, RegressionSolution.Model.GetEstimation(point));
    }
    #endregion

    #region Helpers
    private Individual GetIndividual(RealVector r) {
      var scope = new Scope();
      scope.Variables.Add(new Variable(Algorithm.Problem.Encoding.Name, r));
      return new SingleEncodingIndividual(Algorithm.Problem.Encoding, scope);
    }

    public void UpdateCancellation(CancellationToken cancellationToken) {
      Cancellation = cancellationToken;
    }
    #endregion
  }
}