Changeset 15436

Timestamp:

10/27/17 16:19:11 (7 years ago)

Author:

gkronber

Message:

#2789: added parameter properties to GAM

File:

• branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs (modified) (7 diffs)

branches/MathNetNumerics-Exploration-2789/HeuristicLab.Algorithms.DataAnalysis.Experimental/GAM.cs

@@ -44 +44 @@
   [StorableClass]
   public sealed class GAM : FixedDataAnalysisAlgorithm<IRegressionProblem> {
+
+    private const string LambdaParameterName = "Lambda";
+    private const string MaxIterationsParameterName = "Max iterations";
+    private const string MaxInteractionsParameterName = "Max interactions";
+
+    public IFixedValueParameter<DoubleValue> LambdaParameter {
+      get { return (IFixedValueParameter<DoubleValue>)Parameters[LambdaParameterName]; }
+    }
+    public IFixedValueParameter<IntValue> MaxIterationsParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[MaxIterationsParameterName]; }
+    }
+    public IFixedValueParameter<IntValue> MaxInteractionsParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[MaxInteractionsParameterName]; }
+    }
+
+    public double Lambda {
+      get { return LambdaParameter.Value.Value; }
+      set { LambdaParameter.Value.Value = value; }
+    }
+    public int MaxIterations {
+      get { return MaxIterationsParameter.Value.Value; }
+      set { MaxIterationsParameter.Value.Value = value; }
+    }
+    public int MaxInteractions {
+      get { return MaxInteractionsParameter.Value.Value; }
+      set { MaxInteractionsParameter.Value.Value = value; }
+    }
+
     [StorableConstructor]
     private GAM(bool deserializing) : base(deserializing) { }
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {      
+    private void AfterDeserialization() {
     }
 
@@ -60 +88 @@
       : base() {
       Problem = new RegressionProblem();
-      Parameters.Add(new ValueParameter<DoubleValue>("Lambda", "Regularization for smoothing splines", new DoubleValue(1.0)));
-      Parameters.Add(new ValueParameter<IntValue>("Max iterations", "", new IntValue(100)));
-      Parameters.Add(new ValueParameter<IntValue>("Max interactions", "", new IntValue(1)));
-    }    
+      Parameters.Add(new FixedValueParameter<DoubleValue>(LambdaParameterName, "Regularization for smoothing splines", new DoubleValue(1.0)));
+      Parameters.Add(new FixedValueParameter<IntValue>(MaxIterationsParameterName, "", new IntValue(100)));
+      Parameters.Add(new FixedValueParameter<IntValue>(MaxInteractionsParameterName, "", new IntValue(1)));
+    }
+
 
     protected override void Run(CancellationToken cancellationToken) {
-
-      double lambda = ((IValueParameter<DoubleValue>)Parameters["Lambda"]).Value.Value;
-      int maxIters = ((IValueParameter<IntValue>)Parameters["Max iterations"]).Value.Value;
-      int maxInteractions = ((IValueParameter<IntValue>)Parameters["Max interactions"]).Value.Value;
+      double lambda = Lambda;
+      int maxIters = MaxIterations ;
+      int maxInteractions = MaxInteractions;
       if (maxInteractions < 1 || maxInteractions > 5) throw new ArgumentException("Max interactions is outside the valid range [1 .. 5]");
 
@@ -80 +108 @@
       var inputVars = Problem.ProblemData.AllowedInputVariables.ToArray();
       var nTerms = inputVars.Length; // LR
-      for(int i=1;i<=maxInteractions;i++) {
+      for (int i = 1; i <= maxInteractions; i++) {
         nTerms += inputVars.Combinations(i).Count();
       }
-      IRegressionModel[] f = new IRegressionModel[nTerms]; 
-      for(int i=0;i<f.Length;i++) {
+      IRegressionModel[] f = new IRegressionModel[nTerms];
+      for (int i = 0; i < f.Length; i++) {
         f[i] = new ConstantModel(0.0, problemData.TargetVariable);
       }
@@ -116 +144 @@
         }
 
-        for(int interaction = 1; interaction <= maxInteractions;interaction++) {
+        for (int interaction = 1; interaction <= maxInteractions; interaction++) {
           var selectedVars = HeuristicLab.Common.EnumerableExtensions.Combinations(inputVars, interaction);
 
@@ -141 +169 @@
       var model = new RegressionEnsembleModel(f.Concat(new[] { new ConstantModel(avgY, problemData.TargetVariable) }));
       model.AverageModelEstimates = false;
-      Results.Add(new Result("Ensemble solution", model.CreateRegressionSolution((IRegressionProblemData)problemData.Clone())));
-
+      var solution = model.CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
+      Results.Add(new Result("Ensemble solution", solution));
     }
 
@@ -187 +215 @@
       if (inputVars.All(problemData.Dataset.VariableHasType<double>)) {
         var product = problemData.Dataset.GetDoubleValues(inputVars.First(), problemData.TrainingIndices).ToArray();
-        for(int i = 1;i<inputVars.Length;i++) {
+        for (int i = 1; i < inputVars.Length; i++) {
           product = product.Zip(problemData.Dataset.GetDoubleValues(inputVars[i], problemData.TrainingIndices), (pi, vi) => pi * vi).ToArray();
         }
@@ -210 +238 @@
         pd.TestPartition.End = problemData.TestPartition.End;
         double rmsError, oobRmsError;
-        double avgRelError, oobAvgRelError; 
+        double avgRelError, oobAvgRelError;
         return RandomForestRegression.CreateRandomForestRegressionModel(pd, 100, 0.5, 0.5, 1234, out rmsError, out avgRelError, out oobRmsError, out oobAvgRelError);
       } else return new ConstantModel(target.Average(), problemData.TargetVariable);