protected override void Run(CancellationToken cancellationToken)
{
double rmsError, avgRelError, outOfBagRmsError, outOfBagAvgRelError;
if (SetSeedRandomly)
{
Seed = Random.RandomSeedGenerator.GetSeed();
}
var model = CreateRandomForestRegressionModel(Problem.ProblemData, NumberOfTrees, R, M, Seed,
out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the random forest regression solution on the training set.", new DoubleValue(rmsError)));
Results.Add(new Result("Average relative error", "The average of relative errors of the random forest regression solution on the training set.", new PercentValue(avgRelError)));
Results.Add(new Result("Root mean square error (out-of-bag)", "The out-of-bag root of the mean of squared errors of the random forest regression solution.", new DoubleValue(outOfBagRmsError)));
Results.Add(new Result("Average relative error (out-of-bag)", "The out-of-bag average of relative errors of the random forest regression solution.", new PercentValue(outOfBagAvgRelError)));
IRegressionSolution solution = null;
if (ModelCreation == ModelCreation.Model)
{
solution = model.CreateRegressionSolution(Problem.ProblemData);
}
else if (ModelCreation == ModelCreation.SurrogateModel)
{
var problemData = Problem.ProblemData;
var surrogateModel = new RandomForestModelSurrogate(model, problemData.TargetVariable, problemData, Seed, NumberOfTrees, R, M);
solution = surrogateModel.CreateRegressionSolution(problemData);
}
if (solution != null)
{
Results.Add(new Result(RandomForestRegressionModelResultName, "The random forest regression solution.", solution));
}
}
private RandomForestModelSurrogate(RandomForestModelSurrogate original, Cloner cloner) : base(original, cloner)
{
IRandomForestModel clonedModel = null;
if (original.actualModel.IsValueCreated)
{
clonedModel = cloner.Clone(original.ActualModel);
}
actualModel = new Lazy <IRandomForestModel>(CreateLazyInitFunc(clonedModel)); // only capture clonedModel in the closure
// clone data which is necessary to rebuild the model
this.originalTrainingData = cloner.Clone(original.originalTrainingData);
this.seed = original.seed;
this.classValues = original.classValues;
this.nTrees = original.nTrees;
this.r = original.r;
this.m = original.m;
}
