Changeset 12872 for branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs

Timestamp:

08/17/15 18:33:31 (9 years ago)

Author:

gkronber

Message:

#2434 reverse merge of r12871 (changes should be applied directly to trunk)

File:

• branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs (modified) (4 diffs)

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs

@@ -82 +82 @@
       get { return (FixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
     }
-    public IConstrainedValueParameter<ILossFunction> LossFunctionParameter {
-      get { return (IConstrainedValueParameter<ILossFunction>)Parameters[LossFunctionParameterName]; }
+    public IConstrainedValueParameter<StringValue> LossFunctionParameter {
+      get { return (IConstrainedValueParameter<StringValue>)Parameters[LossFunctionParameterName]; }
     }
     public IFixedValueParameter<IntValue> UpdateIntervalParameter {
@@ -164 +164 @@
       Parameters[CreateSolutionParameterName].Hidden = true;
 
-      var lossFunctions = ApplicationManager.Manager.GetInstances<ILossFunction>();
-      Parameters.Add(new ConstrainedValueParameter<ILossFunction>(LossFunctionParameterName, "The loss function", new ItemSet<ILossFunction>(lossFunctions)));
-      LossFunctionParameter.Value = LossFunctionParameter.ValidValues.First(f => f.ToString().Contains("Squared")); // squared error loss is the default
-    }
-
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      // BackwardsCompatibility3.4
-      #region Backwards compatible code, remove with 3.5
-      // parameter type has been changed
-      var lossFunctionParam = Parameters[LossFunctionParameterName] as ConstrainedValueParameter<StringValue>;
-      if (lossFunctionParam != null) {
-        Parameters.Remove(LossFunctionParameterName);
-        var selectedValue = lossFunctionParam.Value; // to be restored below
-
-        var lossFunctions = ApplicationManager.Manager.GetInstances<ILossFunction>();
-        Parameters.Add(new ConstrainedValueParameter<ILossFunction>(LossFunctionParameterName, "The loss function", new ItemSet<ILossFunction>(lossFunctions)));
-        // try to restore selected value
-        var selectedLossFunction =
-          LossFunctionParameter.ValidValues.FirstOrDefault(f => f.ToString() == selectedValue.Value);
-        if (selectedLossFunction != null) {
-          LossFunctionParameter.Value = selectedLossFunction;
-        } else {
-          LossFunctionParameter.Value = LossFunctionParameter.ValidValues.First(f => f.ToString().Contains("Squared")); // default: SE
-        }
-      }
-      #endregion
-    }
+      var lossFunctionNames = ApplicationManager.Manager.GetInstances<ILossFunction>().Select(l => new StringValue(l.ToString()).AsReadOnly());
+      Parameters.Add(new ConstrainedValueParameter<StringValue>(LossFunctionParameterName, "The loss function", new ItemSet<StringValue>(lossFunctionNames)));
+      LossFunctionParameter.ActualValue = LossFunctionParameter.ValidValues.First(l => l.Value.Contains("Squared")); // squared error loss is the default
+    }
+
 
     protected override void Run(CancellationToken cancellationToken) {
@@ -210 +187 @@
       // init
       var problemData = (IRegressionProblemData)Problem.ProblemData.Clone();
-      var lossFunction = LossFunctionParameter.Value;
+      var lossFunction = ApplicationManager.Manager.GetInstances<ILossFunction>()
+        .Single(l => l.ToString() == LossFunctionParameter.Value.Value);
       var state = GradientBoostedTreesAlgorithmStatic.CreateGbmState(problemData, lossFunction, (uint)Seed, MaxSize, R, M, Nu);
 
@@ -255 +233 @@
       // produce solution 
       if (CreateSolution) {
-        var surrogateModel = new GradientBoostedTreesModelSurrogate(problemData, (uint)Seed, lossFunction,
+        var surrogateModel = new GradientBoostedTreesModelSurrogate(problemData, (uint)Seed, lossFunction.ToString(),
           Iterations, MaxSize, R, M, Nu, state.GetModel());