Changeset 7272 for trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation

Timestamp:

01/04/12 18:59:53 (13 years ago)

Author:

gkronber

Message:

#1737 added online calculator for the mean error and results for the mean error of regression solutions on the training and test partitions.

Location:

trunk/sources

Files:

• . (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs (modified) (5 diffs)

trunk/sources

@@ -18 +18 @@
 bin
 protoc.exe
+*.user

@@ -18 +18 @@
 bin
 protoc.exe
+*.user

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

@@ -40 +40 @@
     private const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
     private const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
+    private const string TrainingMeanErrorResultName = "Mean error (training)";
+    private const string TestMeanErrorResultName = "Mean error (test)";
 
     public new IRegressionModel Model {
@@ -96 +98 @@
       get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
       private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
+    }
+    public double TrainingMeanError {
+      get { return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value; }
+    }
+    public double TestMeanError {
+      get { return ((DoubleValue)this[TestMeanErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value; }
     }
     #endregion
@@ -116 +126 @@
       Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the training partition", new DoubleValue()));
       Add(new Result(TestNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the test partition", new DoubleValue()));
+      Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
+      Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
     }
 
@@ -136 +148 @@
         double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndizes), out errorState);
         TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
+      }
+
+      if (!ContainsKey(TrainingMeanErrorResultName)) {
+        OnlineCalculatorError errorState;
+        Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
+        double trainingME = OnlineMeanErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes), out errorState);
+        TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
+      }
+      if (!ContainsKey(TestMeanErrorResultName)) {
+        OnlineCalculatorError errorState;
+        Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
+        double testME = OnlineMeanErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndizes), out errorState);
+        TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
       }
       #endregion
@@ -171 +196 @@
       double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
       TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
+
+      double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
+      double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
     }
   }

@@ -18 +18 @@
 bin
 protoc.exe
+*.user