Changeset 5885 for trunk/sources/HeuristicLab.Problems.DataAnalysis

Timestamp:

03/30/11 13:57:33 (14 years ago)

Author:

gkronber

Message:

#1418 added R² and MSE as results for discriminant function classification solutions.

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs (modified) (7 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs

@@ -26 +26 @@
 using HeuristicLab.Core;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
 
 namespace HeuristicLab.Problems.DataAnalysis {
@@ -34 +36 @@
   [Item("DiscriminantFunctionClassificationSolution", "Represents a classification solution that uses a discriminant function and classification thresholds.")]
   public class DiscriminantFunctionClassificationSolution : ClassificationSolution, IDiscriminantFunctionClassificationSolution {
+    private const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
+    private const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
+    private const string TrainingRSquaredResultName = "Pearson's R² (training)";
+    private const string TestRSquaredResultName = "Pearson's R² (test)";
+
     public new IDiscriminantFunctionClassificationModel Model {
       get { return (IDiscriminantFunctionClassificationModel)base.Model; }
@@ -47 +54 @@
     }
 
+    public double TrainingMeanSquaredError {
+      get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
+    }
+
+    public double TestMeanSquaredError {
+      get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
+    }
+
+    public double TrainingRSquared {
+      get { return ((DoubleValue)this[TrainingRSquaredResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingRSquaredResultName].Value).Value = value; }
+    }
+
+    public double TestRSquared {
+      get { return ((DoubleValue)this[TestRSquaredResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestRSquaredResultName].Value).Value = value; }
+    }
+
     [StorableConstructor]
     protected DiscriminantFunctionClassificationSolution(bool deserializing) : base(deserializing) { }
@@ -58 +85 @@
     public DiscriminantFunctionClassificationSolution(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
       : base(model, problemData) {
+      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(TrainingRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
+      Add(new Result(TestRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
       RegisterEventHandler();
       SetAccuracyMaximizingThresholds();
+      RecalculateResults();
     }
 
@@ -65 +97 @@
     private void AfterDeserialization() {
       RegisterEventHandler();
+    }
+
+    protected void RecalculateResults() {
+      double[] estimatedTrainingValues = EstimatedTrainingValues.ToArray(); // cache values
+      IEnumerable<double> originalTrainingValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes);
+      double[] estimatedTestValues = EstimatedTestValues.ToArray(); // cache values
+      IEnumerable<double> originalTestValues = ProblemData.Dataset.GetEnumeratedVariableValues(ProblemData.TargetVariable, ProblemData.TestIndizes);
+
+      double trainingMSE = OnlineMeanSquaredErrorEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues);
+      double testMSE = OnlineMeanSquaredErrorEvaluator.Calculate(estimatedTestValues, originalTestValues);
+      double trainingR2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedTrainingValues, originalTrainingValues);
+      double testR2 = OnlinePearsonsRSquaredEvaluator.Calculate(estimatedTestValues, originalTestValues);
+
+      TrainingMeanSquaredError = trainingMSE;
+      TestMeanSquaredError = testMSE;
+      TrainingRSquared = trainingR2;
+      TestRSquared = testR2;
     }
 
@@ -95 +144 @@
       base.OnModelChanged(e);
       SetAccuracyMaximizingThresholds();
+      RecalculateResults();
     }
 
@@ -100 +150 @@
       base.OnProblemDataChanged(e);
       SetAccuracyMaximizingThresholds();
+      RecalculateResults();
     }
     protected virtual void OnModelThresholdsChanged(EventArgs e) {
       base.OnModelChanged(e);
+      RecalculateResults();
     }