Changeset 6647 for branches/GP.Grammar.Editor/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression

Timestamp:

08/09/11 11:01:08 (13 years ago)

Author:

mkommend

Message:

#1479: Updated grammar editor branch.

File:

• branches/GP.Grammar.Editor/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs (modified) (5 diffs)

branches/GP.Grammar.Editor/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs

@@ -32 +32 @@
     private const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
     private const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
+    private const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
+    private const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
     private const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
     private const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
@@ -62 +64 @@
       get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
       private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
+    }
+    public double TrainingMeanAbsoluteError {
+      get { return ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value = value; }
+    }
+    public double TestMeanAbsoluteError {
+      get { return ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value = value; }
     }
     public double TrainingRSquared {
@@ -98 +108 @@
       Add(new Result(TrainingMeanSquaredErrorResultName, "Mean of squared errors of the model on the training partition", new DoubleValue()));
       Add(new Result(TestMeanSquaredErrorResultName, "Mean of squared errors of the model on the test partition", new DoubleValue()));
+      Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
+      Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
       Add(new Result(TrainingSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
       Add(new Result(TestSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
@@ -104 +116 @@
       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()));
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.4
+
+      #region Backwards compatible code, remove with 3.5
+
+      if (!ContainsKey(TrainingMeanAbsoluteErrorResultName)) {
+        OnlineCalculatorError errorState;
+        Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
+        double trainingMAE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes), out errorState);
+        TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
+      }
+
+      if (!ContainsKey(TestMeanAbsoluteErrorResultName)) {
+        OnlineCalculatorError errorState;
+        Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
+        double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes), out errorState);
+        TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
+      }
+      #endregion
     }
 
@@ -117 +151 @@
       double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
       TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
+
+      double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);
+      TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
+      double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedTestValues, originalTestValues, out errorState);
+      TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
 
       double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(estimatedTrainingValues, originalTrainingValues, out errorState);