Changeset 4678 for branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification

Timestamp:

10/29/10 19:26:56 (14 years ago)

Author:

gkronber

Message:

Refactored cloning in DataAnalysis plugins. #922

Location:

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3

Files:

• ClassificationProblemData.cs (modified) (2 diffs)
• SingleObjectiveClassificationProblem.cs (modified) (1 diff)
• Symbolic/Analyzer/ValidationBestSymbolicClassificationSolutionAnalyzer.cs (modified) (4 diffs)
• Symbolic/Evaluators/SymbolicClassificationMeanSquaredErrorEvaluator.cs (modified) (2 diffs)
• Symbolic/Evaluators/SymbolicClassificationPearsonRSquaredEvaluator.cs (modified) (2 diffs)
• Symbolic/SymbolicClassificationProblem.cs (modified) (3 diffs)
• Symbolic/SymbolicClassificationSolution.cs (modified) (3 diffs)

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/ClassificationProblemData.cs

@@ -196 +196 @@
     }
 
+    [StorableConstructor]
+    protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
+    protected ClassificationProblemData(ClassificationProblemData original, Cloner cloner)
+      : base(original, cloner) {
+      RegisterParameterEvents();
+      UpdateClassValues();
+    }
     public ClassificationProblemData()
       : base(new Dataset(defaultInputs, defaultData), defaultInputs, defaultInputs[defaultInputs.Length - 1], 0, 60, 60, 120) {
@@ -207 +214 @@
     }
 
-    [StorableConstructor]
-    protected ClassificationProblemData(bool deserializing) : base(deserializing) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new ClassificationProblemData(this, cloner);
+    }
+
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserializationHook() {
+    private void AfterDeserialization() {
       RegisterParameterEvents();
       RegisterParameterValueEvents();
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      ClassificationProblemData clone = (ClassificationProblemData)base.Clone(cloner);
-      clone.RegisterParameterEvents();
-      clone.UpdateClassValues();
-      return clone;
     }
 

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/SingleObjectiveClassificationProblem.cs

@@ -49 +49 @@
     [StorableConstructor]
     protected SingleObjectiveClassificationProblem(bool deserializing) : base(deserializing) { }
+    protected SingleObjectiveClassificationProblem(SingleObjectiveClassificationProblem<T, U> original, Cloner cloner)
+      : base(original, cloner) {
+      Initialize();
+    }
     public SingleObjectiveClassificationProblem()
       : base() {
       Parameters.Add(new ValueParameter<ClassificationProblemData>(ClassificationProblemDataParameterName, "The data set, target variable and input variables of the data analysis problem."));
       ClassificationProblemData = new ClassificationProblemData();
-      RegisterParameterEvents();
-      RegisterParameterValueEvents();
+      Initialize();
     }
 
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserializationHook() {
+    private void AfterDeserialization() {
+      Initialize();
+    }
+    private void Initialize() {
       RegisterParameterEvents();
       RegisterParameterValueEvents();
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      SingleObjectiveClassificationProblem<T, U> clone = (SingleObjectiveClassificationProblem<T, U>)base.Clone(cloner);
-      clone.RegisterParameterEvents();
-      clone.RegisterParameterValueEvents();
-      return clone;
     }
 

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/Symbolic/Analyzer/ValidationBestSymbolicClassificationSolutionAnalyzer.cs

@@ -23 +23 @@
 using System.Linq;
 using HeuristicLab.Analysis;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
@@ -180 +181 @@
     #endregion
 
+    [StorableConstructor]
+    private ValidationBestSymbolicClassificationSolutionAnalyzer(bool deserializing) : base(deserializing) { }
+    protected ValidationBestSymbolicClassificationSolutionAnalyzer(ValidationBestSymbolicClassificationSolutionAnalyzer original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public ValidationBestSymbolicClassificationSolutionAnalyzer()
       : base() {
@@ -204 +210 @@
     }
 
-    [StorableConstructor]
-    private ValidationBestSymbolicClassificationSolutionAnalyzer(bool deserializing) : base(deserializing) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new ValidationBestSymbolicClassificationSolutionAnalyzer(this, cloner);
+    }
 
     public override IOperation Apply() {
@@ -304 +311 @@
       }
     }
-
   }
 }

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/Symbolic/Evaluators/SymbolicClassificationMeanSquaredErrorEvaluator.cs

@@ -20 +20 @@
 #endregion
 
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -27 +28 @@
   [Item("SymbolicClassificationMeanSquaredErrorEvaluator", "Calculates the mean squared error of a symbolic classification solution.")]
   [StorableClass]
-  public class SymbolicClassifacitionMeanSquaredErrorEvaluator : SymbolicRegressionMeanSquaredErrorEvaluator, ISymbolicClassificationEvaluator {
+  public sealed class SymbolicClassifacitionMeanSquaredErrorEvaluator : SymbolicRegressionMeanSquaredErrorEvaluator, ISymbolicClassificationEvaluator {
     public ClassificationProblemData ClassificationProblemData {
       get { return (ClassificationProblemData)RegressionProblemData; }
     }
 
-    public SymbolicClassifacitionMeanSquaredErrorEvaluator()
-      : base() {
+    [StorableConstructor]
+    private SymbolicClassifacitionMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
+    private SymbolicClassifacitionMeanSquaredErrorEvaluator(SymbolicClassifacitionMeanSquaredErrorEvaluator original, Cloner cloner) : base(original, cloner) { }
+    public SymbolicClassifacitionMeanSquaredErrorEvaluator() : base() { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicClassifacitionMeanSquaredErrorEvaluator(this, cloner);
     }
   }

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/Symbolic/Evaluators/SymbolicClassificationPearsonRSquaredEvaluator.cs

@@ -20 +20 @@
 #endregion
 
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -27 +28 @@
   [Item("SymbolicClassificationPearsonRSquaredEvaluator", "Calculates the pearson R² correlation coefficient of a symbolic classification solution.")]
   [StorableClass]
-  public class SymbolicClassificationPearsonRSquaredEvaluator : SymbolicRegressionPearsonsRSquaredEvaluator, ISymbolicClassificationEvaluator {
+  public sealed class SymbolicClassificationPearsonRSquaredEvaluator : SymbolicRegressionPearsonsRSquaredEvaluator, ISymbolicClassificationEvaluator {
     public ClassificationProblemData ClassificationProblemData {
       get { return (ClassificationProblemData)RegressionProblemData; }
     }
 
-    public SymbolicClassificationPearsonRSquaredEvaluator()
-      : base() {
+    [StorableConstructor]
+    private SymbolicClassificationPearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
+    private SymbolicClassificationPearsonRSquaredEvaluator(SymbolicClassificationPearsonRSquaredEvaluator original, Cloner cloner) : base(original, cloner) { }
+    public SymbolicClassificationPearsonRSquaredEvaluator() : base() { }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicClassificationPearsonRSquaredEvaluator(this, cloner);
     }
   }

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/Symbolic/SymbolicClassificationProblem.cs

@@ -42 +42 @@
   [StorableClass]
   [Creatable("Problems")]
-  public class SymbolicClassificationProblem : SingleObjectiveClassificationProblem<ISymbolicClassificationEvaluator, ISymbolicExpressionTreeCreator>, IStorableContent {
+  public sealed class SymbolicClassificationProblem : SingleObjectiveClassificationProblem<ISymbolicClassificationEvaluator, ISymbolicExpressionTreeCreator>, IStorableContent {
     private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
     private const string FunctionTreeGrammarParameterName = "FunctionTreeGrammar";
@@ -133 +133 @@
 
     [StorableConstructor]
-    protected SymbolicClassificationProblem(bool deserializing) : base(deserializing) { }
+    private SymbolicClassificationProblem(bool deserializing) : base(deserializing) { }
+    private SymbolicClassificationProblem(SymbolicClassificationProblem original, Cloner cloner)
+      : base(original, cloner) {
+      RegisterParameterEvents();
+
+      UpdateEstimationLimits();
+      ParameterizeEvaluator();
+      ParameterizeSolutionCreator();
+      ParameterizeGrammar();
+      ParameterizeOperators();
+      ParameterizeAnalyzers();
+    }
     public SymbolicClassificationProblem()
       : base() {
@@ -167 +178 @@
     }
 
-
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicClassificationProblem(this, cloner);
     }
 

branches/CloningRefactoring/HeuristicLab.Problems.DataAnalysis.Classification/3.3/Symbolic/SymbolicClassificationSolution.cs

@@ -35 +35 @@
   [StorableClass]
   public class SymbolicClassificationSolution : SymbolicRegressionSolution, IClassificationSolution {
+    public new ClassificationProblemData ProblemData {
+      get { return (ClassificationProblemData)base.ProblemData; }
+      set { base.ProblemData = value; }
+    }
+
+    #region properties
+    private List<double> optimalThresholds;
+    private List<double> actualThresholds;
+    public IEnumerable<double> Thresholds {
+      get {
+        if (actualThresholds == null) RecalculateEstimatedValues();
+        return actualThresholds;
+      }
+      set {
+        if (actualThresholds != null && actualThresholds.SequenceEqual(value))
+          return;
+        actualThresholds = new List<double>(value);
+        OnThresholdsChanged();
+      }
+    }
+
+    public IEnumerable<double> EstimatedClassValues {
+      get { return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
+    }
+
+    public IEnumerable<double> EstimatedTrainingClassValues {
+      get { return GetEstimatedClassValues(ProblemData.TrainingIndizes); }
+    }
+
+    public IEnumerable<double> EstimatedTestClassValues {
+      get { return GetEstimatedClassValues(ProblemData.TestIndizes); }
+    }
+
+    [StorableConstructor]
+    protected SymbolicClassificationSolution(bool deserializing) : base(deserializing) { }
+    protected SymbolicClassificationSolution(SymbolicClassificationSolution original, Cloner cloner) : base(original, cloner) { }
     private SymbolicClassificationSolution() : base() { }
     public SymbolicClassificationSolution(ClassificationProblemData problemData, SymbolicRegressionModel model, double lowerEstimationLimit, double upperEstimationLimit)
@@ -40 +76 @@
     }
 
-    public new ClassificationProblemData ProblemData {
-      get { return (ClassificationProblemData)base.ProblemData; }
-      set { base.ProblemData = value; }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicClassificationSolution(this, cloner);
     }
 
@@ -114 +149 @@
     }
 
-    #region properties
-    private List<double> optimalThresholds;
-    private List<double> actualThresholds;
-    public IEnumerable<double> Thresholds {
-      get {
-        if (actualThresholds == null) RecalculateEstimatedValues();
-        return actualThresholds;
-      }
-      set {
-        if (actualThresholds != null && actualThresholds.SequenceEqual(value))
-          return;
-        actualThresholds = new List<double>(value);
-        OnThresholdsChanged();
-      }
-    }
-
-    public IEnumerable<double> EstimatedClassValues {
-      get { return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
-    }
-
-    public IEnumerable<double> EstimatedTrainingClassValues {
-      get { return GetEstimatedClassValues(ProblemData.TrainingIndizes); }
-    }
-
-    public IEnumerable<double> EstimatedTestClassValues {
-      get { return GetEstimatedClassValues(ProblemData.TestIndizes); }
-    }
-
     public IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows) {
       double[] classValues = ProblemData.SortedClassValues.ToArray();