Changeset 13921 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModelSurrogate.cs

Timestamp:

06/19/16 19:56:11 (8 years ago)

Author:

bburlacu

Message:

#2604: Revert changes to DataAnalysisSolution and IDataAnalysisSolution and implement the desired properties in model classes that implement IDataAnalysisModel, IRegressionModel and IClassificationModel.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModelSurrogate.cs (modified) (5 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModelSurrogate.cs

@@ -22 +22 @@
 
 using System.Collections.Generic;
+using System.Linq;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
@@ -54 +55 @@
     private int maxSize;
 
+    public string TargetVariable {
+      get { return trainingProblemData.TargetVariable; }
+    }
+
+    public IEnumerable<string> VariablesUsedForPrediction {
+      get { return actualModel.Models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); }
+    }
 
     [StorableConstructor]
@@ -73 +81 @@
 
     // create only the surrogate model without an actual model
-    public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed, ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
+    public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
+      ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
       : base("Gradient boosted tree model", string.Empty) {
       this.trainingProblemData = trainingProblemData;
@@ -86 +95 @@
 
     // 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(IRegressionProblemData trainingProblemData, uint seed,
+      ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu,
+      IGradientBoostedTreesModel model)
       : this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu) {
       this.actualModel = model;
@@ -104 +115 @@
       return new RegressionSolution(this, (IRegressionProblemData)problemData.Clone());
     }
-
 
     private IGradientBoostedTreesModel RecalculateModel() {