Changeset 11883 for branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberofVariablesEvaluator.cs

Timestamp:

02/04/15 11:16:41 (9 years ago)

Author:

mkommend

Message:

#2175: Additional calculation of results for EuroCast 2015.

File:

• branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberofVariablesEvaluator.cs (copied) (copied from branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator.cs) (3 diffs)

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredNumberofVariablesEvaluator.cs

@@ -26 +26 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Pearson R² & Nested Tree size Evaluator", "Calculates the Pearson R² and the nested tree size of a symbolic regression solution.")]
+  [Item("Pearson R² & Number of Variables Evaluator", "Calculates the Pearson R² and the number of used variables of a symbolic regression solution.")]
   [StorableClass]
-  public class SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
+  public class PearsonRSquaredNumberOfVariablesEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
+    private const string useConstantOptimizationParameterName = "Use constant optimization";
+    public IFixedValueParameter<BoolValue> UseConstantOptimizationParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters[useConstantOptimizationParameterName]; }
+    }
+    public bool UseConstantOptimization {
+      get { return UseConstantOptimizationParameter.Value.Value; }
+      set { UseConstantOptimizationParameter.Value.Value = value; }
+    }
+
     [StorableConstructor]
-    protected SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
-    protected SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator(SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator original, Cloner cloner)
+    protected PearsonRSquaredNumberOfVariablesEvaluator(bool deserializing) : base(deserializing) { }
+    protected PearsonRSquaredNumberOfVariablesEvaluator(PearsonRSquaredNumberOfVariablesEvaluator original, Cloner cloner)
       : base(original, cloner) {
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator(this, cloner);
+      return new PearsonRSquaredNumberOfVariablesEvaluator(this, cloner);
     }
 
-    public SymbolicRegressionMultiObjectivePearsonRSquaredNestedTreeSizeEvaluator() : base() { }
+    public PearsonRSquaredNumberOfVariablesEvaluator()
+      : base() {
+      Parameters.Add(new FixedValueParameter<BoolValue>(useConstantOptimizationParameterName, "", new BoolValue(false)));
+    }
 
     public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } }
@@ -48 +61 @@
       IEnumerable<int> rows = GenerateRowsToEvaluate();
       var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var problemData = ProblemDataParameter.ActualValue;
+      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
+      var estimationLimits = EstimationLimitsParameter.ActualValue;
+      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
+
+      if (UseConstantOptimization) {
+        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, 5, estimationLimits.Upper, estimationLimits.Lower);
+      }
       double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value);
       QualitiesParameter.ActualValue = new DoubleArray(qualities);
@@ -55 +76 @@
     public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling) {
       double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
-      return new double[2] { r2, solution.IterateNodesPostfix().Sum(n => n.GetLength()) };
+      return new double[2] { r2, solution.IterateNodesPostfix().OfType<VariableTreeNode>().Count() };
     }