Free cookie consent management tool by TermsFeed Policy Generator

Ignore:
Timestamp:
07/13/21 10:55:09 (3 years ago)
Author:
gkronber
Message:

#3087: merged r17784:18004 from trunk to branch to prepare for trunk reintegration (fixed a conflict in CrossValidation.cs)

Location:
branches/3087_Ceres_Integration
Files:
6 edited

Legend:

Unmodified
Added
Removed

  • branches/3087_Ceres_Integration

  • branches/3087_Ceres_Integration/HeuristicLab.Tests

  • branches/3087_Ceres_Integration/HeuristicLab.Tests/HeuristicLab.Scripting-3.3/GridSearchRFClassificationScriptTest.cs

    r17180 r18006  
    5959
    6060      var bestSolution = ScriptingUtils.GetVariable<IClassificationSolution>(script, BestSolutionVariableName);
    61       Assert.AreEqual(0.85179407176287, bestSolution.TrainingAccuracy, 1E-8);
    62       Assert.AreEqual(0.81875, bestSolution.TestAccuracy, 1E-8);
     61      Assert.AreEqual(0.858034321372855, bestSolution.TrainingAccuracy, 1E-8);
     62      Assert.AreEqual(0.821875, bestSolution.TestAccuracy, 1E-8);
    6363    }
    6464

  • branches/3087_Ceres_Integration/HeuristicLab.Tests/HeuristicLab.Scripting-3.3/GridSearchRFRegressionScriptTest.cs

    r17180 r18006  
    5959
    6060      var bestSolution = ScriptingUtils.GetVariable<IRegressionSolution>(script, BestSolutionVariableName);
    61       Assert.AreEqual(0.968329534139836, bestSolution.TrainingRSquared, 1E-8);
    62       Assert.AreEqual(0.982380790563445, bestSolution.TestRSquared, 1E-8);
     61      Assert.AreEqual(0.964218622548574, bestSolution.TrainingRSquared, 1E-8);
     62      Assert.AreEqual(0.985989017766796, bestSolution.TestRSquared, 1E-8);
    6363    }
    6464

  • branches/3087_Ceres_Integration/HeuristicLab.Tests/HeuristicLab.Scripting-3.3/Script Sources/GridSearchRFClassificationScriptSource.cs

    r12292 r18006  
    3838    double rmsError, outOfBagRmsError, relClassificationError, outOfBagRelClassificationError;
    3939    bestParameters = RandomForestUtil.GridSearch(problemData, numberOfFolds, shuffleFolds, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
    40     var model = RandomForestModel.CreateClassificationModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
    41                                                             out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
     40    var model = RandomForestClassification.CreateRandomForestClassificationModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
     41                                                                                 out rmsError, out relClassificationError, out outOfBagRmsError, out outOfBagRelClassificationError);
    4242    return (RandomForestClassificationSolution)model.CreateClassificationSolution(problemData);
    4343  }
    … …  
    4646    double rmsError, outOfBagRmsError, relClassificationError, outOfBagRelClassificationError;
    4747    bestParameters = RandomForestUtil.GridSearch(problemData, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
    48     var model = RandomForestModel.CreateClassificationModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
    49                                                             out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
     48    var model = RandomForestClassification.CreateRandomForestClassificationModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
     49                                                                                 out rmsError, out relClassificationError, out outOfBagRmsError, out outOfBagRelClassificationError);
    5050    return (RandomForestClassificationSolution)model.CreateClassificationSolution(problemData);
    5151  }

  • branches/3087_Ceres_Integration/HeuristicLab.Tests/HeuristicLab.Scripting-3.3/Script Sources/GridSearchRFRegressionScriptSource.cs

    r12292 r18006  
    1414  /* Number of crossvalidation folds: */
    1515  const int numberOfFolds = 3;
    16   /* Specify whether the crossvalidation folds should be shuffled */
    17   const bool shuffleFolds = true;
    1816
    1917  /* The tunable Random Forest parameters:
    … …  
    3735  private static RandomForestRegressionSolution GridSearchWithCrossvalidation(IRegressionProblemData problemData, out RFParameter bestParameters, int seed = 3141519) {
    3836    double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
    39     bestParameters = RandomForestUtil.GridSearch(problemData, numberOfFolds, shuffleFolds, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
    40     var model = RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed, out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
     37    bestParameters = RandomForestUtil.GridSearch(problemData, numberOfFolds, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
     38    var model = RandomForestRegression.CreateRandomForestRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
     39                                                                         out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
    4140    return (RandomForestRegressionSolution)model.CreateRegressionSolution(problemData);
    4241  }
    … …  
    4645    var random = new MersenneTwister();
    4746    bestParameters = RandomForestUtil.GridSearch(problemData, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
    48     var model = RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
    49                                                         out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
     47    var model = RandomForestRegression.CreateRandomForestRegressionModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
     48                                                                         out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
    5049    return (RandomForestRegressionSolution)model.CreateRegressionSolution(problemData);
    5150  }
Note: See TracChangeset for help on using the changeset viewer.