Changeset 15614 for branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/LeafTypes/GaussianProcessLeaf.cs

Timestamp:

01/15/18 08:21:48 (6 years ago)

Author:

bwerth

Message:

#2847 made changes to M5 according to review comments

Location:

branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis

Files:

• . (modified) (1 prop)
• 3.4 (modified) (1 prop)
• 3.4/M5Regression/LeafTypes/GaussianProcessLeaf.cs (modified) (3 diffs)

branches/M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/LeafTypes/GaussianProcessLeaf.cs

@@ -33 +33 @@
   [StorableClass]
   [Item("GaussianProcessLeaf", "A leaf type that uses gaussian process models as leaf models.")]
-  public class GaussianProcessLeaf : ParameterizedNamedItem, ILeafType<IGaussianProcessModel> {
+  public class GaussianProcessLeaf : ParameterizedNamedItem, ILeafModel {
     #region ParameterNames
     public const string TriesParameterName = "Tries";
@@ -75 +75 @@
 
     #region IModelType
-    public IGaussianProcessModel BuildModel(IRegressionProblemData pd, IRandom random, CancellationToken cancellation, out int noParameters) {
-      if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a linear model");
+    public bool ProvidesConfidence {
+      get { return true; }
+    }
+    public IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int noParameters) {
+      if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a gaussian process model");
       Regression.Problem = new RegressionProblem {ProblemData = pd};
       var cvscore = double.MaxValue;
@@ -82 +85 @@
 
       for (var i = 0; i < Tries; i++) {
-        var res = M5StaticUtilities.RunSubAlgorithm(Regression, random.Next(), cancellation);
+        var res = M5StaticUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
         var t = res.Select(x => x.Value).OfType<GaussianProcessRegressionSolution>().FirstOrDefault();
-        var score = ((DoubleValue) res["Negative log pseudo-likelihood (LOO-CV)"].Value).Value;
+        var score = ((DoubleValue)res["Negative log pseudo-likelihood (LOO-CV)"].Value).Value;
         if (score >= cvscore || t == null || double.IsNaN(t.TrainingRSquared)) continue;
         cvscore = score;
         sol = t;
       }
-
+      Regression.Runs.Clear();
       if (sol == null) throw new ArgumentException("Could not create Gaussian Process model");