Changeset 17045 for branches/2952_RF-ModelStorage/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees

Timestamp:

06/27/19 15:46:20 (6 years ago)

Author:

mkommend

Message:

#2952: Intermediate commit of refactoring RF models that is not yet finished.

Location:

branches/2952_RF-ModelStorage/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees

Files:

• GradientBoostedTreesAlgorithm.cs (modified) (1 diff)
• GradientBoostedTreesModel.cs (modified) (2 diffs)
• GradientBoostedTreesModelSurrogate.cs (modified) (2 diffs)

branches/2952_RF-ModelStorage/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs

@@ -269 +269 @@
 
         if (ModelCreation == ModelCreation.SurrogateModel) {
-          model = new GradientBoostedTreesModelSurrogate(problemData, (uint)Seed, lossFunction, Iterations, MaxSize, R, M, Nu, (GradientBoostedTreesModel)model);
+          model = new GradientBoostedTreesModelSurrogate((GradientBoostedTreesModel)model, problemData, (uint)Seed, lossFunction, Iterations, MaxSize, R, M, Nu);
         }
 

branches/2952_RF-ModelStorage/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModel.cs

@@ -34 +34 @@
   // this is essentially a collection of weighted regression models
   public sealed class GradientBoostedTreesModel : RegressionModel, IGradientBoostedTreesModel {
-    // BackwardsCompatibility3.4 for allowing deserialization & serialization of old models
-    #region Backwards compatible code, remove with 3.5
-
     [Storable(Name = "models")]
     private IList<IRegressionModel> __persistedModels {
@@ -53 +50 @@
       get { return weights; }
     }
-    #endregion
 
     public override IEnumerable<string> VariablesUsedForPrediction {

branches/2952_RF-ModelStorage/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModelSurrogate.cs

@@ -90 +90 @@
     private Func<IGradientBoostedTreesModel> CreateLazyInitFunc(IGradientBoostedTreesModel clonedModel) {
       return () => {
-        return clonedModel == null ? RecalculateModel() : clonedModel;
+        return clonedModel ?? RecalculateModel();
       };
     }
 
     // create only the surrogate model without an actual model
-    public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
+    private GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
       ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
       : base(trainingProblemData.TargetVariable, "Gradient boosted tree model", string.Empty) {
@@ -106 +106 @@
       this.m = m;
       this.nu = nu;
+
+      actualModel = new Lazy<IGradientBoostedTreesModel>(() => RecalculateModel());
     }
 
     // wrap an actual model in a surrograte
-    public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
-      ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu,
-      IGradientBoostedTreesModel model)
+    public GradientBoostedTreesModelSurrogate(IGradientBoostedTreesModel model, IRegressionProblemData trainingProblemData, uint seed,
+      ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
       : this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu) {
       actualModel = new Lazy<IGradientBoostedTreesModel>(() => model);