source: branches/HeuristicLab.EvolutionTracking/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisUsefulGenesAnalyzer.cs @ 13495

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Analyzers {
  [StorableClass]
  [Item("SymbolicDataAnalysisUsefulGenesAnalyzer", "An analyzer which performs pruning by promoting genes in the population that outperform their containing individuals (the individuals are replaced by their subparts).")]
  public class SymbolicDataAnalysisUsefulGenesAnalyzer : SingleSuccessorOperator, ISymbolicDataAnalysisAnalyzer {
    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
    private const string QualityParameterName = "Quality";
    private const string ResultCollectionParameterName = "Results";
    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
    private const string ProblemDataParameterName = "ProblemData";
    private const string GenerationsParameterName = "Generations";
    private const string UpdateCounterParameterName = "UpdateCounter";
    private const string UpdateIntervalParameterName = "UpdateInterval";
    private const string MinimumGenerationsParameterName = "MinimumGenerations";
    private const string PruningProbabilityParameterName = "PruningProbability";
    private const string PercentageOfBestSolutionsParameterName = "PercentageOfBestSolutions";
    private const string PromotedSubtreesResultName = "Promoted subtrees";
    private const string AverageQualityImprovementResultName = "Average quality improvement";
    private const string AverageLengthReductionResultName = "Average length reduction";
    private const string RandomParameterName = "Random";

    #region Parameters
    public IScopeTreeLookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
      get { return (IScopeTreeLookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
    }

    public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
      get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters[QualityParameterName]; }
    }

    public ILookupParameter<IRandom> RandomParameter {
      get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
    }

    public ILookupParameter<ResultCollection> ResultCollectionParameter {
      get { return (ILookupParameter<ResultCollection>)Parameters[ResultCollectionParameterName]; }
    }

    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
    }

    public ILookupParameter<IDataAnalysisProblemData> ProblemDataParameter {
      get { return (ILookupParameter<IDataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
    }

    public ILookupParameter<IntValue> GenerationsParameter {
      get { return (ILookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
    }

    public ValueParameter<IntValue> UpdateCounterParameter {
      get { return (ValueParameter<IntValue>)Parameters[UpdateCounterParameterName]; }
    }

    public ValueParameter<IntValue> UpdateIntervalParameter {
      get { return (ValueParameter<IntValue>)Parameters[UpdateIntervalParameterName]; }
    }

    public ValueParameter<IntValue> MinimumGenerationsParameter {
      get { return (ValueParameter<IntValue>)Parameters[MinimumGenerationsParameterName]; }
    }

    public ValueParameter<PercentValue> PercentageOfBestSolutionsParameter {
      get { return (ValueParameter<PercentValue>)Parameters[PercentageOfBestSolutionsParameterName]; }
    }

    public ValueParameter<PercentValue> PruningProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters[PruningProbabilityParameterName]; }
    }
    #endregion

    #region Parameter properties
    public int UpdateCounter {
      get { return UpdateCounterParameter.Value.Value; }
      set { UpdateCounterParameter.Value.Value = value; }
    }

    public int UpdateInterval {
      get { return UpdateIntervalParameter.Value.Value; }
      set { UpdateIntervalParameter.Value.Value = value; }
    }

    public int MinimumGenerations {
      get { return MinimumGenerationsParameter.Value.Value; }
      set { MinimumGenerationsParameter.Value.Value = value; }
    }

    public double PercentageOfBestSolutions {
      get { return PercentageOfBestSolutionsParameter.Value.Value; }
      set { PercentageOfBestSolutionsParameter.Value.Value = value; }
    }

    public double PruningProbability {
      get { return PruningProbabilityParameter.Value.Value; }
      set { PruningProbabilityParameter.Value.Value = value; }
    }
    #endregion

    public SymbolicDataAnalysisUsefulGenesAnalyzer() {
      #region Add parameters
      Parameters.Add(new ScopeTreeLookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName));
      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>(QualityParameterName));
      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName));
      Parameters.Add(new LookupParameter<ResultCollection>(ResultCollectionParameterName));
      Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName));
      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName));
      Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName));
      Parameters.Add(new ValueParameter<IntValue>(UpdateCounterParameterName, new IntValue(0)));
      Parameters.Add(new ValueParameter<IntValue>(UpdateIntervalParameterName, new IntValue(1)));
      Parameters.Add(new ValueParameter<IntValue>(MinimumGenerationsParameterName, "The minimum number of generations the algorithm must be let to evolve before applying this analyzer.", new IntValue(50)));
      Parameters.Add(new ValueParameter<PercentValue>(PercentageOfBestSolutionsParameterName, "How many of the best individuals should be pruned using this method.", new PercentValue(1.0)));
      Parameters.Add(new ValueParameter<PercentValue>(PruningProbabilityParameterName, "The probability to apply pruning", new PercentValue(0.1)));
      #endregion
    }

    protected SymbolicDataAnalysisUsefulGenesAnalyzer(SymbolicDataAnalysisUsefulGenesAnalyzer original, Cloner cloner)
      : base(original, cloner) { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicDataAnalysisUsefulGenesAnalyzer(this, cloner);
    }

    [StorableConstructor]
    protected SymbolicDataAnalysisUsefulGenesAnalyzer(bool deserializing)
      : base(deserializing) {
    }

    public bool EnabledByDefault {
      get { return false; }
    }

    public override void InitializeState() {
      UpdateCounter = 0;
      base.InitializeState();
    }

    public override IOperation Apply() {
      int generations = GenerationsParameter.ActualValue.Value;
      #region Update counter & update interval
      if (generations < MinimumGenerations)
        return base.Apply();
      UpdateCounter++;
      if (UpdateCounter != UpdateInterval) {
        return base.Apply();
      }
      UpdateCounter = 0;
      #endregion

      var trees = SymbolicExpressionTreeParameter.ActualValue.ToArray();
      var qualities = QualityParameter.ActualValue.ToArray();

      Array.Sort(qualities, trees); // sort trees array based on qualities

      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
      var problemData = (IRegressionProblemData)ProblemDataParameter.ActualValue;
      var rows = problemData.TrainingIndices.ToList();
      var random = RandomParameter.ActualValue;

      int replacedTrees = 0;
      int avgLengthReduction = 0;
      double avgQualityImprovement = 0;

      var count = (int)Math.Floor(trees.Length * PercentageOfBestSolutions);

      for (int i = trees.Length - 1; i >= trees.Length - count; --i) {
        if (random.NextDouble() > PruningProbability) continue;
        var tree = trees[i];
        var quality = qualities[i].Value;
        var root = tree.Root.GetSubtree(0).GetSubtree(0);

        foreach (var s in root.IterateNodesPrefix().Skip(1)) {
          var r2 = EvaluateSubtree(s, interpreter, problemData, rows);
          if (double.IsNaN(r2) || r2 <= quality) continue;
          avgQualityImprovement += (r2 - quality);
          avgLengthReduction += (tree.Length - s.GetLength());
          replacedTrees++;
          // replace tree with its own subtree
          var startNode = tree.Root.GetSubtree(0);
          startNode.RemoveSubtree(0);
          startNode.AddSubtree(s);
          // update tree quality
          qualities[i].Value = r2;

          break;
        }
      }

      avgQualityImprovement = replacedTrees == 0 ? 0 : avgQualityImprovement / replacedTrees;
      avgLengthReduction = replacedTrees == 0 ? 0 : (int)Math.Round((double)avgLengthReduction / replacedTrees);

      var results = ResultCollectionParameter.ActualValue;
      DataTable table;
      if (results.ContainsKey(PromotedSubtreesResultName)) {
        table = (DataTable)results[PromotedSubtreesResultName].Value;
      } else {
        table = new DataTable(PromotedSubtreesResultName);
        table.Rows.Add(new DataRow(PromotedSubtreesResultName));
        results.Add(new Result(PromotedSubtreesResultName, table));
      }
      table.Rows[PromotedSubtreesResultName].Values.Add(replacedTrees);

      if (results.ContainsKey(AverageQualityImprovementResultName)) {
        table = (DataTable)results[AverageQualityImprovementResultName].Value;
      } else {
        table = new DataTable(AverageQualityImprovementResultName);
        table.Rows.Add(new DataRow(AverageQualityImprovementResultName));
        results.Add(new Result(AverageQualityImprovementResultName, table));
      }
      table.Rows[AverageQualityImprovementResultName].Values.Add(avgQualityImprovement);

      if (results.ContainsKey(AverageLengthReductionResultName)) {
        table = (DataTable)results[AverageLengthReductionResultName].Value;
      } else {
        table = new DataTable(AverageLengthReductionResultName);
        table.Rows.Add(new DataRow(AverageLengthReductionResultName));
        results.Add(new Result(AverageLengthReductionResultName, table));
      }
      table.Rows[AverageLengthReductionResultName].Values.Add(avgLengthReduction);

      return base.Apply();
    }

    private static double EvaluateSubtree(ISymbolicExpressionTreeNode subtree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IRegressionProblemData problemData, List<int> rows) {
      var linearInterpreter = (SymbolicDataAnalysisExpressionTreeLinearInterpreter)interpreter;
      var dataset = problemData.Dataset;

      var targetValues = dataset.GetDoubleValues(problemData.TargetVariable, rows);
      var estimatedValues = linearInterpreter.GetValues(subtree, dataset, rows);

      OnlineCalculatorError error;
      double r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out error);
      return (error == OnlineCalculatorError.None) ? r * r : double.NaN;
    }
  }
}