Changeset 5501 for branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective

Timestamp:

02/17/11 01:19:27 (14 years ago)

Author:

mkommend

Message:

#1418: Corrected namespaces and added symbolic classification specific evaluators.

Location:

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective

Files:

• . (added)
• SymbolicClassificationSingleObjectiveEvaluator.cs (copied) (copied from branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs) (1 diff)
• SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs (copied) (copied from branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs) (2 diffs)
• SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs (copied) (copied from branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs) (2 diffs)

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveEvaluator.cs

@@ -20 +20 @@
 #endregion
 
-
 using HeuristicLab.Common;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator, ISymbolicRegressionEvaluator {
-    public new IRegressionProblemData ProblemData {
-      get { return (IRegressionProblemData)base.ProblemData; }
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
+  public abstract class SymbolicClassificationSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator, ISymbolicClassificationSingleObjectiveEvaluator {
+    public new IClassificationProblemData ProblemData {
+      get { return (IClassificationProblemData)base.ProblemData; }
     }
 
     [StorableConstructor]
-    protected SymbolicRegressionSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { }
-    protected SymbolicRegressionSingleObjectiveEvaluator(SymbolicRegressionSingleObjectiveEvaluator original, Cloner cloner)
+    protected SymbolicClassificationSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicClassificationSingleObjectiveEvaluator(SymbolicClassificationSingleObjectiveEvaluator original, Cloner cloner)
       : base(original, cloner) {
     }
 
-    protected SymbolicRegressionSingleObjectiveEvaluator()
+    protected SymbolicClassificationSingleObjectiveEvaluator()
       : base() {
 

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Mean squared error evaluator", "Calculates the mean squared error of a symbolic regression solution.")]
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
+  [Item("Mean squared error evaluator", "Calculates the mean squared error of a symbolic classification solution.")]
   [StorableClass]
-  public class SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
+  public class SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
     [StorableConstructor]
-    protected SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
-    protected SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner)
+    protected SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner)
       : base(original, cloner) {
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
+      return new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
     }
 
@@ -47 +47 @@
     }
 
-    public static double Calculate(ISymbolicDataAnalysisTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
+    public static double Calculate(ISymbolicDataAnalysisTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.SingleObjective.Evaluators {
-  [Item("Pearson R² evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")]
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
+  [Item("Pearson R² evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic classification solution.")]
   [StorableClass]
-  public class SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
+  public class SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
     [StorableConstructor]
-    protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
-    protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
+    protected SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
       : base(original, cloner) {
     }
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(this, cloner);
+      return new SymbolicClassificationSingleObjectivePearsonRSquaredEvaluator(this, cloner);
     }
 
@@ -47 +47 @@
     }
 
-    public static double Calculate(ISymbolicDataAnalysisTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
+    public static double Calculate(ISymbolicDataAnalysisTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
       IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);