#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.Generic;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [Item("Constraint ConstOpt Evaluator", "")]
  [StorableType("4170FD8B-DDD9-43B7-8BF8-F9C7290D4D1C")]
  public class SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
    [StorableConstructor]
    protected SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator(StorableConstructorFlag _) : base(_) { }
    protected SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator(SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator(this, cloner);
    }

    public SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator() : base() { }

    public override bool Maximization { get { return true; } }

    public override IOperation InstrumentedApply() {
      var solution = SymbolicExpressionTreeParameter.ActualValue;
      IEnumerable<int> rows = GenerateRowsToEvaluate();

      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
      QualityParameter.ActualValue = new DoubleValue(quality);
      return base.InstrumentedApply();
    }

    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
      OnlineCalculatorError errorState = OnlineCalculatorError.None;

      var constraints = IntervalConstraintsParser.Parse(((RegressionProblemData)problemData).IntervalConstraintsParameter.Value.Value);
      var intervalInterpreter = new IntervalInterpreter();
      var variableRanges = ((RegressionProblemData)problemData).VariableRangesParameter.Value.VariableIntervals;


      var r2 = SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices, applyLinearScaling, 10);
      if (HasConstraintVioluations(constraints, intervalInterpreter, variableRanges, tree)) {
        r2 = 0;
      }
      return r2;
    }


    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree,
      IRegressionProblemData problemData, IEnumerable<int> rows) {
      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
      EstimationLimitsParameter.ExecutionContext = context;
      ApplyLinearScalingParameter.ExecutionContext = context;

      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
        EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows,
        ApplyLinearScalingParameter.ActualValue.Value);

      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
      EstimationLimitsParameter.ExecutionContext = null;
      ApplyLinearScalingParameter.ExecutionContext = null;

      return r2;
    }

    private static bool HasConstraintVioluations(IEnumerable<IntervalConstraint> constraints, IntervalInterpreter intervalInterpreter,
      Dictionary<string, Interval> variableRanges, ISymbolicExpressionTree solution) {
      foreach (var constraint in constraints) {
        if (constraint.Variable != null && !variableRanges.ContainsKey(constraint.Variable))
          throw new ArgumentException($"The given variable {constraint.Variable} in the constraint does not exists in the model.", nameof(IntervalConstraintsParser));
        if (!constraint.IsDerivation) {
          var res = intervalInterpreter.GetSymbolicExpressionTreeInterval(solution, variableRanges);
          if (!constraint.Interval.Contains(res, constraint.InclusiveLowerBound,
            constraint.InclusiveUpperBound)) {
            return true;
          }
        } else {
          var tree = solution;
          for (var i = 0; i < constraint.NumberOfDerivation; ++i) {
            tree = DerivativeCalculator.Derive(tree, constraint.Variable);
          }
          var res = intervalInterpreter.GetSymbolicExpressionTreeInterval(tree, variableRanges);
          if (!constraint.Interval.Contains(res, constraint.InclusiveLowerBound,
            constraint.InclusiveUpperBound)) {
            return true;
          }
        }
      }
      return false;
    }
  }
}