source: branches/FitnessLandscapeAnalysis/HeuristicLab.Analysis.FitnessLandscape/Analysis/Aggregators/EvolvabilityAggregator.cs @ 11813

using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Data;
using HeuristicLab.Common.Resources;

namespace HeuristicLab.Analysis.FitnessLandscape {

  [Item("Evolvability Aggregator", "Aggregates evolvability values into an evolvability portrait.")]
  [StorableClass]
  public class EvolvabilityAggregator : NamedItem, IAggregator {

    #region Fields
    [Storable]
    private List<double> E_a;
    [Storable]
    private List<double> E_b;
    [Storable]
    private List<double> E_c;
    [Storable]
    private List<double> E_d;
    [Storable]
    private List<double> baseQualities;
    [Storable]
    private List<double> expectedAbsoluteQualityDeltas;
    [Storable]
    private List<double> expectedRelativeQualityDeltas;
    #endregion    

    #region Properties
    public static new Image StaticItemImage { get { return VSImageLibrary.Database; } }
    public override bool CanChangeName { get { return false; } }
    #endregion

    #region Construction and Cloning
    [StorableConstructor]
    protected EvolvabilityAggregator(bool deserializing) : base(deserializing) { }
    protected EvolvabilityAggregator(EvolvabilityAggregator original, Cloner cloner)
      : base(original, cloner) {
      E_a = original.E_a.ToList();
      E_b = original.E_b.ToList();
      E_c = original.E_c.ToList();
      E_d = original.E_d.ToList();
      baseQualities = original.baseQualities.ToList();
      expectedAbsoluteQualityDeltas = original.expectedAbsoluteQualityDeltas.ToList();
      expectedRelativeQualityDeltas = original.expectedRelativeQualityDeltas.ToList();
    }
    public EvolvabilityAggregator() {
      name = ItemName;
      description = ItemDescription;
      E_a = new List<double>();
      E_b = new List<double>();
      E_c = new List<double>();
      E_d = new List<double>();
      baseQualities = new List<double>();
      expectedAbsoluteQualityDeltas = new List<double>();
      expectedRelativeQualityDeltas = new List<double>();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new EvolvabilityAggregator(this, cloner);
    }
    #endregion

    #region IAggregator Members
    public void MaybeAddResult(IResult result) {
      if (result.DataType == typeof(DoubleValue)) {
        double value = ((DoubleValue)result.Value).Value;
        switch (result.Name) {
          case "E_a": E_a.Add(value); break;
          case "E_b": E_b.Add(value); break;
          case "E_c": E_c.Add(value); break;
          case "E_d": E_d.Add(value); break;
          case "Base Quality": baseQualities.Add(value); break;
          case "Expected Absolute Quality Delta": expectedAbsoluteQualityDeltas.Add(value); break;
          case "Expected Relative Quality Delta": expectedRelativeQualityDeltas.Add(value); break;
        }
      }
    }

    public void Reset() {
      E_a.Clear();
      E_b.Clear();
      E_c.Clear();
      E_d.Clear();
      baseQualities.Clear();
      expectedAbsoluteQualityDeltas.Clear();
      expectedRelativeQualityDeltas.Clear();
    }

    private class PortraitEntry {
      public readonly double A, B, C, D, Q, AbsQDelta, RelQDelta;
      public PortraitEntry(double a, double b, double c, double d, double q, double absQDelta, double relQDelta) {        
        A = a;
        B = b;
        C = c;
        D = d;
        Q = q;
        AbsQDelta = absQDelta;
        RelQDelta = relQDelta;
      }            
    }

    public IResult CreateResult() {
      var portrait = new DataTable("Evolvability Portrait");
      var E_a_row = new DataRow("E_a", "probability of non-deleterious mutation")
                      {VisualProperties = {SecondYAxis = true}};
      var E_b_row = new DataRow("E_b", "average expected offspring fitness");
      var E_c_row = new DataRow("E_c", "90th percentile offspring fitness");
      var E_d_row = new DataRow("E_d", "10th percentile offspring fitness");
      var baseQualityRow = new DataRow("Q", "Base quality");
      var absQDelta = new DataRow("abs Q delta", "expected absolute quality delta");
      var relQDelta = new DataRow("rel Q delta", "expected relatve quality delta")
                        {VisualProperties = {SecondYAxis = true}};
      var entries = E_a.Select((t, i) => 
        new PortraitEntry(t, E_b[i], E_c[i], E_d[i],
          baseQualities[i], expectedAbsoluteQualityDeltas[i], expectedRelativeQualityDeltas[i]))
          .ToList();
      Action<List<PortraitEntry>> addBinAverages = b => {
        if (b.Count > 0) {
          E_a_row.Values.Add(b.Average(e => e.A));
          E_b_row.Values.Add(b.Average(e => e.B));
          E_c_row.Values.Add(b.Average(e => e.C));
          E_d_row.Values.Add(b.Average(e => e.D));
          baseQualityRow.Values.Add(b.Average(e => e.Q));
          absQDelta.Values.Add(b.Average(e => e.AbsQDelta));
          relQDelta.Values.Add(b.Average(e => e.RelQDelta));
        }
      };
      var a = new DistributionAnalyzer(baseQualities);
      addBinAverages(entries.Where(e => e.Q <= a[0.1]).ToList());
      for (double t = 0.1; t < 1; t += 0.1)
        addBinAverages(entries.Where(e => a[t] < e.Q && e.Q <= a[t+0.1]).ToList());
      portrait.Rows.Add(E_a_row);
      portrait.Rows.Add(E_b_row);
      portrait.Rows.Add(E_c_row);
      portrait.Rows.Add(E_d_row);
      portrait.Rows.Add(baseQualityRow);
      portrait.Rows.Add(absQDelta);
      portrait.Rows.Add(relQDelta);
      return new Result("Evolvability Portrait", portrait);
    }    
    #endregion
  }
}