Changeset 16589 for branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective

Timestamp:

02/05/19 14:50:21 (6 years ago)

Author:

chaider

Message:

#2971

• Added new Evaluator to evaulate Pearson RSquared with repsect to given constraints
• changes in SymbolicRegressionSolution

File:

• branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator.cs (copied) (copied from branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs) (4 diffs)

branches/2971_named_intervals/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using HeuristicLab.Common;
@@ -28 +29 @@
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Pearson R² Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")]
+  [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 SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
+  public class SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     [StorableConstructor]
-    protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
-    protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
+    protected SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator original, Cloner cloner)
       : base(original, cloner) {
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(this, cloner);
+      return new SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator(this, cloner);
     }
 
-    public SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator() : base() { }
+    public SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator() : base() { }
 
     public override bool Maximization { get { return true; } }
@@ -58 +59 @@
       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;
@@ -86 +116 @@
       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;
+    }
   }
 }