source: branches/DataAnalysis.IslandAlgorithms/HeuristicLab.Algorithms.DataAnalysis.Symbolic/3.3/RandomSamplesEvaluator .cs @ 10177

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Random;

namespace HeuristicLab.Algorithms.DataAnalysis.Symbolic {
  [StorableClass]
  public sealed class RandomSamplesEvaluator : SingleSuccessorOperator, IStochasticOperator, ISymbolicDataAnalysisIslandGeneticAlgorithmEvaluator {
    private const string RandomParameterName = "Random";
    private const string ProblemDataParameterName = "ProblemData";
    private const string EvaluatorParameterName = "ProblemEvaluator";
    private const string QualityParameterName = "Quality";
    private const string FitnessCalculationPartitionParameterName = "FitnessCalculationPartition";
    private const string FixedSamplesPartitionParameterName = "FixedSamplesPartition";
    private const string RandomSamplesParameterName = "RandomSamples";

    #region parameter properties
    public ILookupParameter<IRandom> RandomParameter {
      get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
    }
    public ILookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
      get { return (ILookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
    }
    public ILookupParameter<IOperator> EvaluatorParameter {
      get { return (ILookupParameter<IOperator>)Parameters[EvaluatorParameterName]; }
    }
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters[QualityParameterName]; }
    }
    public ILookupParameter<IntRange> FitnessCalculationPartitionParameter {
      get { return (ILookupParameter<IntRange>)Parameters[FitnessCalculationPartitionParameterName]; }
    }
    public ILookupParameter<IntRange> FixedSamplesPartitionParameter {
      get { return (ILookupParameter<IntRange>)Parameters[FixedSamplesPartitionParameterName]; }
    }
    public IFixedValueParameter<IntValue> RandomSamplesParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[RandomSamplesParameterName]; }
    }
    #endregion

    #region properties

    public int RandomSamples {
      get { return RandomSamplesParameter.Value.Value; }
      set { RandomSamplesParameter.Value.Value = value; }
    }
    #endregion

    [StorableConstructor]
    private RandomSamplesEvaluator(bool deserializing) : base(deserializing) { }
    private RandomSamplesEvaluator(RandomSamplesEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new RandomSamplesEvaluator(this, cloner);
    }

    public RandomSamplesEvaluator()
      : base() {
      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
      Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
      Parameters.Add(new LookupParameter<IOperator>(EvaluatorParameterName, "The evaluator provided by the symbolic data analysis  problem."));
      Parameters.Add(new LookupParameter<DoubleValue>(QualityParameterName, "The quality which is calculated by the encapsulated evaluator."));
      Parameters.Add(new LookupParameter<IntRange>(FitnessCalculationPartitionParameterName, "The data partition used to calculate the fitness"));
      Parameters.Add(new LookupParameter<IntRange>(FixedSamplesPartitionParameterName, "The data partition which is used to calculate the fitness on the fixed samples."));
      Parameters.Add(new FixedValueParameter<IntValue>(RandomSamplesParameterName, "The number of random samples used for fitness calculation in each island.", new IntValue()));
    }

    public override IOperation Apply() {
      var evaluator = EvaluatorParameter.ActualValue;
      var problemData = ProblemDataParameter.ActualValue;

      var samples = FitnessCalculationPartitionParameter.ActualValue;
      var fixedSamples = FixedSamplesPartitionParameter.ActualValue;
      var randomSamples = RandomSamples;

      //create fixed rows enumerable
      var rows = Enumerable.Range(fixedSamples.Start, fixedSamples.Size);
      //create randomly chosen rows enumerable
      if (randomSamples > 0) {
        if (randomSamples > samples.Size - fixedSamples.Size) {
          var error = string.Format("Could not select {0} random samples, because there are {1} total samples present from which {2} where used in the fixed partition. Please lower the number of random samples in the algorithm configuration.", randomSamples, samples.Size, fixedSamples.Size);
          throw new OperatorExecutionException(this, error);
        }
        var randomRows = Enumerable.Range(samples.Start, samples.Size).Where(r => r < fixedSamples.Start || r >= fixedSamples.End);
        randomRows = randomRows.SampleRandomWithoutRepetition(RandomParameter.ActualValue, randomSamples, samples.Size - fixedSamples.Size);

        rows = rows.Concat(randomRows);
      }
      //filter out test rows
      rows = rows.Where(r => r < problemData.TestPartition.Start || r > problemData.TestPartition.End);

      //execution context is created manually to be able to clear the rows parameter easily
      var executionContext = new ExecutionContext(ExecutionContext, evaluator, ExecutionContext.Scope);

      //TODO change to lookup parameter
      executionContext.Scope.Variables.Remove("Rows");
      executionContext.Scope.Variables.Add(new HeuristicLab.Core.Variable("Rows", new EnumerableItem<int>(rows)));
      var successor = evaluator.Execute(executionContext, this.CancellationToken);
      return new OperationCollection(successor, base.Apply());
    }
  }
}