Changeset 6643

Timestamp:

08/08/11 17:47:46 (13 years ago)

Author:

mkommend

Message:

#1613: Added !MAE to RegressionSolutions.

Location:

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4

Files:

• HeuristicLab.Problems.DataAnalysis-3.4.csproj (modified) (1 diff)
• Implementation/Regression/RegressionSolutionBase.cs (modified) (5 diffs)
• Interfaces/Regression/IRegressionSolution.cs (modified) (1 diff)
• OnlineCalculators/OnlineMeanAbsoluteErrorCalculator.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanSquaredErrorCalculator.cs) (5 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj

@@ -139 +139 @@
     <Compile Include="Interfaces\Regression\IRegressionEnsembleSolution.cs" />
     <Compile Include="Implementation\Regression\RegressionSolutionBase.cs" />
+    <Compile Include="OnlineCalculators\OnlineMeanAbsoluteErrorCalculator.cs" />
     <Compile Include="OnlineCalculators\OnlineLinearScalingParameterCalculator.cs" />
     <Compile Include="Implementation\Classification\DiscriminantFunctionClassificationModel.cs" />

trunk/sources/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);

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Regression/IRegressionSolution.cs

@@ -33 +33 @@
     double TrainingMeanSquaredError { get; }
     double TestMeanSquaredError { get; }
+    double TrainingMeanAbsoluteError { get; }
+    double TestMeanAbsoluteError { get; }
     double TrainingRSquared { get; }
     double TestRSquared { get; }

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineMeanAbsoluteErrorCalculator.cs

@@ -24 +24 @@
 
 namespace HeuristicLab.Problems.DataAnalysis {
-  public class OnlineMeanSquaredErrorCalculator : IOnlineCalculator {
+  public class OnlineMeanAbsoluteErrorCalculator : IOnlineCalculator {
 
-    private double sse;
+    private double sae;
     private int n;
-    public double MeanSquaredError {
+    public double MeanAbsoluteError {
       get {
-        return n > 0 ? sse / n : 0.0;
+        return n > 0 ? sae / n : 0.0;
       }
     }
 
-    public OnlineMeanSquaredErrorCalculator() {
+    public OnlineMeanAbsoluteErrorCalculator() {
       Reset();
     }
@@ -44 +44 @@
     }
     public double Value {
-      get { return MeanSquaredError; }
+      get { return MeanAbsoluteError; }
     }
     public void Reset() {
       n = 0;
-      sse = 0.0;
+      sae = 0.0;
       errorState = OnlineCalculatorError.InsufficientElementsAdded;
     }
@@ -58 +58 @@
       } else {
         double error = estimated - original;
-        sse += error * error;
+        sae += Math.Abs(error);
         n++;
         errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
@@ -68 +68 @@
       IEnumerator<double> firstEnumerator = first.GetEnumerator();
       IEnumerator<double> secondEnumerator = second.GetEnumerator();
-      OnlineMeanSquaredErrorCalculator mseCalculator = new OnlineMeanSquaredErrorCalculator();
+      OnlineMeanAbsoluteErrorCalculator maeCalculator = new OnlineMeanAbsoluteErrorCalculator();
 
       // always move forward both enumerators (do not use short-circuit evaluation!)
@@ -74 +74 @@
         double estimated = secondEnumerator.Current;
         double original = firstEnumerator.Current;
-        mseCalculator.Add(original, estimated);
-        if (mseCalculator.ErrorState != OnlineCalculatorError.None) break;
+        maeCalculator.Add(original, estimated);
+        if (maeCalculator.ErrorState != OnlineCalculatorError.None) break;
       }
 
       // check if both enumerators are at the end to make sure both enumerations have the same length
-      if (mseCalculator.ErrorState == OnlineCalculatorError.None &&
+      if (maeCalculator.ErrorState == OnlineCalculatorError.None &&
          (secondEnumerator.MoveNext() || firstEnumerator.MoveNext())) {
         throw new ArgumentException("Number of elements in first and second enumeration doesn't match.");
       } else {
-        errorState = mseCalculator.ErrorState;
-        return mseCalculator.MeanSquaredError;
+        errorState = maeCalculator.ErrorState;
+        return maeCalculator.MeanAbsoluteError;
       }
     }