Changeset 13184 for stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs

Timestamp:

11/16/15 19:49:40 (8 years ago)

Author:

gkronber

Message:

#2450: merged r12868,r12873,r12875,r13065:13066,r13157:13158 from trunk to stable

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs (modified) (5 diffs)

stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs

@@ -52 +52 @@
       internal RegressionTreeBuilder treeBuilder { get; private set; }
 
+      private readonly uint randSeed;
       private MersenneTwister random { get; set; }
 
@@ -71 +72 @@
         this.m = m;
 
+        this.randSeed = randSeed;
         random = new MersenneTwister(randSeed);
         this.problemData = problemData;
@@ -99 +101 @@
 
       public IRegressionModel GetModel() {
-        return new GradientBoostedTreesModel(models, weights);
+#pragma warning disable 618
+        var model = new GradientBoostedTreesModel(models, weights);
+#pragma warning restore 618
+        // we don't know the number of iterations here but the number of weights is equal 
+        // to the number of iterations + 1 (for the constant model)
+        // wrap the actual model in a surrogate that enables persistence and lazy recalculation of the model if necessary
+        return new GradientBoostedTreesModelSurrogate(problemData, randSeed, lossFunction, weights.Count - 1, maxSize, r, m, nu, model);
       }
       public IEnumerable<KeyValuePair<string, double>> GetVariableRelevance() {
@@ -122 +130 @@
 
     // simple interface
-    public static IRegressionSolution TrainGbm(IRegressionProblemData problemData, ILossFunction lossFunction, int maxSize, double nu, double r, double m, int maxIterations, uint randSeed = 31415) {
+    public static GradientBoostedTreesSolution TrainGbm(IRegressionProblemData problemData, ILossFunction lossFunction, int maxSize, double nu, double r, double m, int maxIterations, uint randSeed = 31415) {
       Contract.Assert(r > 0);
       Contract.Assert(r <= 1.0);
@@ -135 +143 @@
 
       var model = state.GetModel();
-      return new RegressionSolution(model, (IRegressionProblemData)problemData.Clone());
+      return new GradientBoostedTreesSolution(model, (IRegressionProblemData)problemData.Clone());
     }