#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.EvolutionTracking;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Analyzers {
  [StorableClass]
  [Item("SymbolicDataAnalysisGeneticOperatorImprovementAnalyzer", "An analyzer which records the best and average genetic operator improvement")]
  public class SymbolicDataAnalysisGeneticOperatorImprovementAnalyzer : EvolutionTrackingAnalyzer<ISymbolicExpressionTree> {
    public const string QualityParameterName = "Quality";
    public const string PopulationParameterName = "SymbolicExpressionTree";
    public const string CountIntermediateChildrenParameterName = "CountIntermediateChildren";

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

    public IScopeTreeLookupParameter<ISymbolicExpressionTree> PopulationParameter {
      get { return (IScopeTreeLookupParameter<ISymbolicExpressionTree>)Parameters[PopulationParameterName]; }
    }

    public IFixedValueParameter<BoolValue> CountIntermediateChildrenParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters[CountIntermediateChildrenParameterName]; }
    }

    public bool CountIntermediateChildren {
      get { return CountIntermediateChildrenParameter.Value.Value; }
      set { CountIntermediateChildrenParameter.Value.Value = value; }
    }

    public SymbolicDataAnalysisGeneticOperatorImprovementAnalyzer() {
      Parameters.Add(new ScopeTreeLookupParameter<ISymbolicExpressionTree>(PopulationParameterName, "The population of individuals."));
      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>(QualityParameterName, "The individual qualities."));
      Parameters.Add(new FixedValueParameter<BoolValue>(CountIntermediateChildrenParameterName, "Specifies whether to consider intermediate children (when crossover was followed by mutation). This should be set to false for offspring selection.", new BoolValue(true)));

      CountIntermediateChildrenParameter.Hidden = true;
    }


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

    public SymbolicDataAnalysisGeneticOperatorImprovementAnalyzer(
    SymbolicDataAnalysisGeneticOperatorImprovementAnalyzer original, Cloner cloner) : base(original, cloner) {
      CountIntermediateChildren = original.CountIntermediateChildren;
    }

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

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (!Parameters.ContainsKey(CountIntermediateChildrenParameterName))
        Parameters.Add(new FixedValueParameter<BoolValue>(CountIntermediateChildrenParameterName, "Specifies whether to consider intermediate children (when crossover was followed by mutation", new BoolValue(true)));
      CountIntermediateChildrenParameter.Hidden = true;
    }

    public override IOperation Apply() {
      IntValue updateCounter = UpdateCounterParameter.ActualValue;
      if (updateCounter == null) {
        updateCounter = new IntValue(0);
        UpdateCounterParameter.ActualValue = updateCounter;
      }
      updateCounter.Value++;
      if (updateCounter.Value != UpdateInterval.Value) return base.Apply();
      updateCounter.Value = 0;

      var graph = PopulationGraph;
      if (graph == null || Generation.Value == 0)
        return base.Apply();

      var generation = Generation.Value;
      var averageQuality = QualityParameter.ActualValue.Average(x => x.Value);
      var population = PopulationParameter.ActualValue;
      var populationSize = population.Length;

      //      var vertices = population.Select(graph.GetByContent).ToList();
      var crossoverChildren = new List<IGenealogyGraphNode<ISymbolicExpressionTree>>();
      var mutationChildren = new List<IGenealogyGraphNode<ISymbolicExpressionTree>>();
      var vertices = graph.Vertices.Where(x => x.Rank > generation - 1);
      foreach (var v in vertices) {
        if (v.InDegree == 2) {
          crossoverChildren.Add(v);
        } else {
          var parent = v.Parents.First();
          // mutation is always preceded by crossover
          // so the parent vertex should have an intermediate rank
          // otherwise, it is the previos generation elite
          if (parent.Rank.IsAlmost(generation - 1) && parent.IsElite)
            continue;
          mutationChildren.Add(v);
        }
      }
      DataTable table;
      #region crossover improvement
      if (!Results.ContainsKey("Crossover improvement")) {
        table = new DataTable("Crossover improvement");
        Results.Add(new Result("Crossover improvement", table));
        table.Rows.AddRange(new[] {
          new DataRow("Average crossover child quality") { VisualProperties = { StartIndexZero = true } },
          new DataRow("Average crossover parent quality") { VisualProperties = { StartIndexZero = true } },
          new DataRow("Best crossover child quality") { VisualProperties = { StartIndexZero = true } },
          new DataRow("Best crossover parent quality") { VisualProperties = { StartIndexZero = true } },
        });
      } else {
        table = (DataTable)Results["Crossover improvement"].Value;
      }

      var avgCrossoverParentQuality = crossoverChildren.SelectMany(x => x.Parents).Average(x => x.Quality);
      var avgCrossoverChildQuality = crossoverChildren.Average(x => x.Quality);

      var bestCrossoverChildQuality = crossoverChildren.OrderBy(x => x.Quality).Last().Quality;
      var bestCrossoverParentQuality = crossoverChildren.OrderBy(x => x.Quality).Last().Parents.First().Quality;

      table.Rows["Average crossover child quality"].Values.Add(avgCrossoverChildQuality);
      table.Rows["Average crossover parent quality"].Values.Add(avgCrossoverParentQuality);
      table.Rows["Best crossover child quality"].Values.Add(bestCrossoverChildQuality);
      table.Rows["Best crossover parent quality"].Values.Add(bestCrossoverParentQuality);
      #endregion

      #region mutation improvement
      if (!Results.ContainsKey("Mutation improvement")) {
        table = new DataTable("Mutation improvement");
        Results.Add(new Result("Mutation improvement", table));
        table.Rows.AddRange(new[] {
          new DataRow("Average mutation child quality") { VisualProperties = { StartIndexZero = true } },
          new DataRow("Average mutation parent quality") { VisualProperties = { StartIndexZero = true } },
          new DataRow("Best mutation child quality") { VisualProperties = { StartIndexZero = true } },
          new DataRow("Best mutation parent quality") { VisualProperties = { StartIndexZero = true } },
        });
      } else {
        table = (DataTable)Results["Mutation improvement"].Value;
      }

      var avgMutationParentQuality = mutationChildren.SelectMany(x => x.Parents).Average(x => x.Quality);
      var avgMutationChildQuality = mutationChildren.Average(x => x.Quality);

      var bestMutationChildQuality = mutationChildren.OrderBy(x => x.Quality).Last().Quality;
      var bestMutationParentQuality = mutationChildren.OrderBy(x => x.Quality).Last().Parents.First().Quality;

      table.Rows["Average mutation child quality"].Values.Add(avgMutationChildQuality);
      table.Rows["Average mutation parent quality"].Values.Add(avgMutationParentQuality);
      table.Rows["Best mutation child quality"].Values.Add(bestMutationChildQuality);
      table.Rows["Best mutation parent quality"].Values.Add(bestMutationParentQuality);

      #endregion
      return base.Apply();
    }
  }
}