Index: branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator.cs
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--- branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator.cs	(revision 16589)
+++ branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator.cs	(revision 16589)
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+﻿#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 HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
+  [Item("Pearson R² Constraint Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")]
+  [StorableClass]
+  public class SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
+    [StorableConstructor]
+    protected SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(this, cloner);
+    }
+
+    public SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator() : 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 solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
+      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
+      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+      OnlineCalculatorError errorState;
+
+      IntervalConstraintsParser parser = new IntervalConstraintsParser();
+      var constraints = parser.Parse(((RegressionProblemData) problemData).IntervalConstraintsParameter.Value.Value);
+      var intervalInterpreter = new IntervalInterpreter();
+      var variableRanges = ((RegressionProblemData) problemData).VariableRangesParameter.Value.VariableIntervals;
+
+
+      foreach (var constraint in constraints) {
+        if (!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.GetSymbolicExressionTreeInterval(solution, variableRanges);
+          if (!IntervalInBoundaries(constraint.Interval, res, constraint.InclusiveLowerBound,
+            constraint.InclusiveUpperBound)) {
+            return 0;
+          }
+        } else {
+          var tree = solution;
+          for (var i = 0; i < constraint.NumberOfDerivation; ++i) {
+            tree = DerivativeCalculator.Derive(tree, constraint.Variable);
+          }
+          var res = intervalInterpreter.GetSymbolicExressionTreeInterval(tree, variableRanges);
+          if (!IntervalInBoundaries(constraint.Interval, res, constraint.InclusiveLowerBound,
+            constraint.InclusiveUpperBound)) {
+            return 0;
+          }
+        }
+      }
+
+
+      double r;
+      if (applyLinearScaling) {
+        var rCalculator = new OnlinePearsonsRCalculator();
+        CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows);
+        errorState = rCalculator.ErrorState;
+        r = rCalculator.R;
+      } else {
+        IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
+        r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
+      }
+      if (errorState != OnlineCalculatorError.None) return double.NaN;
+      return r*r;
+    }
+
+    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 IntervalInBoundaries(Interval i1, Interval i2, bool inclusiveLower, bool inclusiveUpper) {
+      if (inclusiveLower) {
+        if (i2.LowerBound < i1.LowerBound)
+          return false;
+      } else {
+        if (i2.LowerBound <= i1.LowerBound)
+          return false;
+      }
+
+      if (inclusiveUpper) {
+        if (i2.UpperBound > i1.UpperBound)
+          return false;
+      } else {
+        if (i2.UpperBound >= i1.UpperBound)
+          return false;
+      }
+
+      return true;
+    }
+  }
+}
