#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.EvolutionTracking;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
  [Item("UpdateQualityOperator", "Put the estimated values of the tree in the scope to be used by the phenotypic similarity calculator")]
  [StorableClass]
  public class UpdateQualityOperator : EvolutionTrackingOperator<ISymbolicExpressionTree> {
    private const string ProblemDataParameterName = "ProblemData";
    private const string InterpreterParameterName = "SymbolicExpressionTreeInterpreter";
    private const string EstimationLimitsParameterName = "EstimationLimits";
    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
    private const string ScaleEstimatedValuesParameterName = "ScaleEstimatedValues";

    #region parameters
    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
      get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
    }
    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter {
      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName]; }
    }
    public ILookupParameter<DoubleLimit> EstimationLimitsParameter {
      get { return (ILookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
    }
    public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
    }
    public ILookupParameter<BoolValue> ScaleEstimatedValuesParameter {
      get { return (ILookupParameter<BoolValue>)Parameters[ScaleEstimatedValuesParameterName]; }
    }
    #endregion

    public UpdateQualityOperator() {
      Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName));
      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(InterpreterParameterName));
      Parameters.Add(new LookupParameter<DoubleLimit>(EstimationLimitsParameterName));
      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName));
      Parameters.Add(new LookupParameter<BoolValue>(ScaleEstimatedValuesParameterName));
    }

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

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

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

    public override IOperation Apply() {
      var tree = SymbolicExpressionTreeParameter.ActualValue;
      var problemData = ProblemDataParameter.ActualValue;
      var estimationLimits = EstimationLimitsParameter.ActualValue;
      var interpreter = InterpreterParameter.ActualValue;

      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, problemData.TrainingIndices);
      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices);

      var scaleEstimatedValues = ScaleEstimatedValuesParameter.ActualValue.Value;

      IEnumerable<double> scaled;
      if (scaleEstimatedValues) {
        var linearScalingCalculator = new OnlineLinearScalingParameterCalculator();

        var e1 = estimatedValues.GetEnumerator();
        var e2 = targetValues.GetEnumerator();

        int count = 0;
        while (e1.MoveNext() && e2.MoveNext()) {
          var estimated = e1.Current;
          var target = e2.Current;
          if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
            linearScalingCalculator.Add(estimated, target);
          ++count;
        }
        if (e1.MoveNext() || e2.MoveNext())
          throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");

        double alpha = linearScalingCalculator.Alpha;
        double beta = linearScalingCalculator.Beta;
        if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) {
          alpha = 0.0;
          beta = 1.0;
        }
        scaled = estimatedValues.Select(x => x * beta + alpha).LimitToRange(estimationLimits.Lower, estimationLimits.Upper);
      } else {
        scaled = estimatedValues.LimitToRange(estimationLimits.Lower, estimationLimits.Upper);
      }

      OnlineCalculatorError error;
      var r = OnlinePearsonsRCalculator.Calculate(targetValues, scaled, out error);
      if (error != OnlineCalculatorError.None) r = double.NaN;

      var r2 = r * r;

      var variables = ExecutionContext.Scope.Variables;

      ((DoubleValue)variables["Quality"].Value).Value = r2;
      //GenealogyGraph.GetByContent(tree).Quality = r2;

      if (variables.ContainsKey("EstimatedValues")) {
        variables["EstimatedValues"].Value = new DoubleArray(scaled.ToArray());
      } else {
        variables.Add(new Core.Variable("EstimatedValues", new DoubleArray(scaled.ToArray())));
      }
      return base.Apply();
    }
  }
}