Changeset 16788 for trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs

Timestamp:

04/16/19 11:43:52 (5 years ago)

Author:

mkommend

Message:

#2361: Merged all changes from the sensitivity branch to trunk.

Location:

trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification

Files:

• . (modified) (1 prop)
• 3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs (copied) (copied from branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs) (8 diffs)

trunk/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HEAL.Attic;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
@@ -28 +29 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.PluginInfrastructure;
 
-namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.SingleObjective {
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
+  [NonDiscoverableType]
   [Item("Weighted Performance Measures Evaluator", "Calculates the quality of a symbolic classification solution based on three weighted measures(normalized mean squared error, false negative rate(1-sensitivity) and false positve rate(1-specificity)).")]
-  [StorableClass]
+  [StorableType("0772F316-5E12-4153-857E-8625069B4677")]
   public class SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator : SymbolicClassificationSingleObjectiveEvaluator {
     private const string NormalizedMeanSquaredErrorWeightingFactorParameterName = "NormalizedMeanSquaredErrorWeightingFactor";
@@ -57 +59 @@
 
     public double NormalizedMeanSquaredErrorWeightingFactor {
-      get {
-        return NormalizedMeanSquaredErrorWeightingFactorParameter.Value.Value;
-      }
+      get { return NormalizedMeanSquaredErrorWeightingFactorParameter.Value.Value; }
     }
     public double FalseNegativeRateWeightingFactor {
-      get {
-        return FalseNegativeRateWeightingFactorParameter.Value.Value;
-      }
+      get { return FalseNegativeRateWeightingFactorParameter.Value.Value; }
     }
     public double FalsePositiveRateWeightingFactor {
-      get {
-        return FalsePositiveRateWeightingFactorParameter.Value.Value;
-      }
+      get { return FalsePositiveRateWeightingFactorParameter.Value.Value; }
     }
 
     [StorableConstructor]
-    protected SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(StorableConstructorFlag _) : base(_) { }
     protected SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator original, Cloner cloner)
       : base(original, cloner) {
@@ -79 +75 @@
     public override IDeepCloneable Clone(Cloner cloner) {
       return new SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator(this, cloner);
-    }
-
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      // BackwardsCompatibility
-      #region Backwards compatible code
-      if (Parameters.ContainsKey(ModelCreatorParameterName)) {
-        Parameters.Remove(ModelCreatorParameterName);
-        Parameters.Add(new ValueLookupParameter<ISymbolicClassificationModelCreator>(ModelCreatorParameterName, "The model creator which is used during the evaluations."));
-      }
-      #endregion
     }
 
@@ -102 +87 @@
     public override IOperation InstrumentedApply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var tree = SymbolicExpressionTreeParameter.ActualValue;
       var creator = ModelCreatorParameter.ActualValue;
-      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
-              ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value, creator, NormalizedMeanSquaredErrorWeightingFactor, FalseNegativeRateWeightingFactor, FalsePositiveRateWeightingFactor);
+      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
+      var estimationLimits = EstimationLimitsParameter.ActualValue;
+      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
+
+
+      double quality = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper,
+              ProblemDataParameter.ActualValue, rows, applyLinearScaling, creator, NormalizedMeanSquaredErrorWeightingFactor, FalseNegativeRateWeightingFactor, FalsePositiveRateWeightingFactor);
       QualityParameter.ActualValue = new DoubleValue(quality);
       return base.InstrumentedApply();
     }
 
-    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData,
+    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData,
                 IEnumerable<int> rows, bool applyLinearScaling, ISymbolicClassificationModelCreator modelCreator, double normalizedMeanSquaredErrorWeightingFactor, double falseNegativeRateWeightingFactor, double falsePositiveRateWeightingFactor) {
-      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
+      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, problemData.Dataset, rows);
       var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
       var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit).ToArray();
@@ -124 +114 @@
       ISymbolicDiscriminantFunctionClassificationModel m;
 
-      var model = modelCreator.CreateSymbolicClassificationModel(solution, interpreter, lowerEstimationLimit, upperEstimationLimit);
+      var model = modelCreator.CreateSymbolicClassificationModel(problemData.TargetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit);
       if ((m = model as ISymbolicDiscriminantFunctionClassificationModel) != null) {
         m.ThresholdCalculator.Calculate(problemData, boundedEstimatedValues, targetClassValues, out classValues, out thresholds);
@@ -133 +123 @@
         estimatedClassValues = model.GetEstimatedClassValues(problemData.Dataset, rows);
       }
+
       var performanceCalculator = new ClassificationPerformanceMeasuresCalculator(positiveClassName, problemData.GetClassValue(positiveClassName));
       performanceCalculator.Calculate(targetClassValues, estimatedClassValues);