Changeset 14927 for branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators

Timestamp:

05/04/17 17:19:35 (8 years ago)

Author:

gkronber

Message:

#2520: changed all usages of StorableClass to use StorableType with an auto-generated GUID (did not add StorableType to other type definitions yet)

Location:

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators

Files:

• SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (1 diff)
• SymbolicRegressionLogResidualEvaluator.cs (modified) (2 diffs)
• SymbolicRegressionMeanRelativeErrorEvaluator.cs (modified) (2 diffs)
• SymbolicRegressionSingleObjectiveEvaluator.cs (modified) (1 diff)
• SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.cs (modified) (1 diff)
• SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs (modified) (1 diff)
• SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs (modified) (2 diffs)
• SymbolicRegressionVarianceModelEvaluator.cs (modified) (1 diff)

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -28 +28 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
   [Item("Constant Optimization Evaluator", "Calculates Pearson R² of a symbolic regression solution and optimizes the constant used.")]
-  [StorableClass]
+  [StorableType("feecded4-f5c9-496d-9150-0e16e3e93f7c")]
   public class SymbolicRegressionConstantOptimizationEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     private const string ConstantOptimizationIterationsParameterName = "ConstantOptimizationIterations";

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionLogResidualEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Log Residual Evaluator", "Evaluator for symbolic regression models that calculates the mean of logarithmic absolute residuals avg(log( 1 + abs(y' - y)))" + 
+  [Item("Log Residual Evaluator", "Evaluator for symbolic regression models that calculates the mean of logarithmic absolute residuals avg(log( 1 + abs(y' - y)))" +
                                   "This evaluator does not perform linear scaling!" +
                                   "This evaluator can be useful if the modeled function contains discontinuities (e.g. 1/x). " +
@@ -38 +38 @@
                                   "This effects GP convergence because functional fragments which are necessary to explain small variations are also more likely" +
                                   " to stay in the population. This is useful even when the actual objective function is mean of squared errors.")]
-  [StorableClass]
+  [StorableType("6a2b4b99-3c19-41e3-adc7-62bb38721cb4")]
   public class SymbolicRegressionLogResidualEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     [StorableConstructor]

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionMeanRelativeErrorEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
@@ -33 +33 @@
                                          "The +1 is necessary to handle data with the value of 0.0 correctly. " +
                                          "Notice: Linear scaling is ignored for this evaluator.")]
-  [StorableClass]
+  [StorableType("a3d19505-1a12-4d4f-932c-0ebc8044562d")]
   public class SymbolicRegressionMeanRelativeErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     public override bool Maximization { get { return false; } }

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [StorableClass]
-  public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>, ISymbolicRegressionSingleObjectiveEvaluator {  
+  [StorableType("6b51b89e-8d04-4736-8ffa-80485607ebfe")]
+  public abstract class SymbolicRegressionSingleObjectiveEvaluator : SymbolicDataAnalysisSingleObjectiveEvaluator<IRegressionProblemData>, ISymbolicRegressionSingleObjectiveEvaluator {
     [StorableConstructor]
     protected SymbolicRegressionSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { }
     protected SymbolicRegressionSingleObjectiveEvaluator(SymbolicRegressionSingleObjectiveEvaluator original, Cloner cloner) : base(original, cloner) { }
-    protected SymbolicRegressionSingleObjectiveEvaluator(): base() {}    
+    protected SymbolicRegressionSingleObjectiveEvaluator() : base() { }
   }
 }

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator.cs

@@ -25 +25 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
   [Item("Maximum absolute error Evaluator", "Calculates the maximum squared error of a symbolic regression solution.")]
-  [StorableClass]
+  [StorableType("744f2ac1-82d4-40d9-b037-beb362793d88")]
   public class SymbolicRegressionSingleObjectiveMaxAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     public override bool Maximization { get { return false; } }

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator.cs

@@ -25 +25 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
   [Item("Mean absolute error Evaluator", "Calculates the mean absolute error of a symbolic regression solution.")]
-  [StorableClass]
+  [StorableType("8be4e783-5141-441e-baac-2d2226979dfd")]
   public class SymbolicRegressionSingleObjectiveMeanAbsoluteErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     public override bool Maximization { get { return false; } }

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs

@@ -25 +25 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
   [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic regression solution.")]
-  [StorableClass]
+  [StorableType("d9975a6a-2db7-4f88-bfc6-b697ba20d7d6")]
   public class SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     public override bool Maximization { get { return false; } }

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

@@ -25 +25 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 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.")]
-  [StorableClass]
+  [StorableType("e067c9dc-cc13-4f93-96c2-aacae751e335")]
   public class SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     [StorableConstructor]
@@ -70 +70 @@
       }
       if (errorState != OnlineCalculatorError.None) return double.NaN;
-      return r*r;
+      return r * r;
     }
 

branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionVarianceModelEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Persistence;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
   [Item("Variance Model Evaluator", "Can be used for modeling variance of a variable. Assumes that the variable values are sampled from a zero mean Gaussian. Use a model for the target variable to calculate the residuals. In a second step use the residuals as the target variable and use this evaluator to create the model for the conditional variance.")]
-  [StorableClass]
+  [StorableType("4a70f22b-b92c-4783-b119-fd50b29f2f38")]
   public class SymbolicRegressionVarianceModelEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
     [StorableConstructor]