Changeset 12302 for branches/SensitivityEvaluator

Timestamp:

04/13/15 11:59:52 (10 years ago)

Author:

ehopf

Message:

#2361: Added a NotSupported-Exception for the case that the user activates the applyLinearScaling-option. Additionally made some execution time measurements with and without invocation of the ToArray-Extension-Method after the calculation of the bounded estimated values.

File:

• branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs (modified) (4 diffs)

branches/SensitivityEvaluator/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SingleObjective/SymbolicClassificationSingleObjectiveWeightedPerformanceMeasuresEvaluator.cs

@@ -22 +22 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
@@ -101 +102 @@
                 IEnumerable<int> rows, bool applyLinearScaling, ISymbolicClassificationModelCreator modelCreator, double normalizedMeanSquaredErrorWeightingFactor, double falseNegativeRateWeightingFactor, double falsePositiveRateWeightingFactor) {
       IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
-      IEnumerable<double> targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
-      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
+      var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows).ToArray();
+      var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit).ToArray();
       OnlineCalculatorError errorState;
       double nmse;
@@ -115 +116 @@
       var estimatedClassValues = model.GetEstimatedClassValues(boundedEstimatedValues);
       performanceCalculator.Calculate(targetClassValues, estimatedClassValues);
-      if (performanceCalculator.ErrorState != OnlineCalculatorError.None) 
+      if (performanceCalculator.ErrorState != OnlineCalculatorError.None)
         return Double.NaN;
       double falseNegativeRate = 1 - performanceCalculator.TruePositiveRate;
@@ -121 +122 @@
 
       if (applyLinearScaling) {
-        var nmseCalculator = new OnlineNormalizedMeanSquaredErrorCalculator();
-        CalculateWithScaling(targetClassValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, nmseCalculator, problemData.Dataset.Rows);
-        errorState = nmseCalculator.ErrorState;
-        nmse = nmseCalculator.NormalizedMeanSquaredError;
-      } else {
-        nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetClassValues, boundedEstimatedValues, out errorState);
+        throw new NotSupportedException("The Weighted Performance Measures Evaluator does not suppport linear scaling!");
       }
+      nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetClassValues, boundedEstimatedValues, out errorState);
       if (errorState != OnlineCalculatorError.None) return Double.NaN;
-
       return normalizedMeanSquaredErrorWeightingFactor * nmse + falseNegativeRateWeightingFactor * falseNegativeRate + falsePositiveRateWeightingFactor * falsePositiveRate;
     }