Changeset 18027 for branches/3026_IntegrationIntoSymSpace/HeuristicLab.Tests/HeuristicLab.Scripting-3.3/Script Sources/GridSearchRFRegressionScriptSource.cs

Timestamp:

07/20/21 18:13:55 (3 years ago)

Author:

dpiringe

Message:

#3026

• merged trunk into branch

Location:

branches/3026_IntegrationIntoSymSpace

Files:

• . (modified) (1 prop)
• HeuristicLab.Tests (modified) (1 prop)
• HeuristicLab.Tests/HeuristicLab.Scripting-3.3/Script Sources/GridSearchRFRegressionScriptSource.cs (modified) (3 diffs)

branches/3026_IntegrationIntoSymSpace/HeuristicLab.Tests/HeuristicLab.Scripting-3.3/Script Sources/GridSearchRFRegressionScriptSource.cs

@@ -14 +14 @@
   /* Number of crossvalidation folds: */
   const int numberOfFolds = 3;
-  /* Specify whether the crossvalidation folds should be shuffled */
-  const bool shuffleFolds = true;
 
   /* The tunable Random Forest parameters:
@@ -37 +35 @@
   private static RandomForestRegressionSolution GridSearchWithCrossvalidation(IRegressionProblemData problemData, out RFParameter bestParameters, int seed = 3141519) {
     double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
-    bestParameters = RandomForestUtil.GridSearch(problemData, numberOfFolds, shuffleFolds, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
-    var model = RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed, out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
+    bestParameters = RandomForestUtil.GridSearch(problemData, numberOfFolds, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
+    var model = RandomForestRegression.CreateRandomForestRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed, 
+                                                                         out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
     return (RandomForestRegressionSolution)model.CreateRegressionSolution(problemData);
   }
@@ -46 +45 @@
     var random = new MersenneTwister();
     bestParameters = RandomForestUtil.GridSearch(problemData, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
-    var model = RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
-                                                        out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
+    var model = RandomForestRegression.CreateRandomForestRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
+                                                                         out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
     return (RandomForestRegressionSolution)model.CreateRegressionSolution(problemData);
   }