/// <summary>
/// Grid search without crossvalidation (since for random forests the out-of-bag estimate is unbiased)
/// </summary>
/// <param name="problemData">The regression problem data</param>
/// <param name="parameterRanges">The ranges for each parameter in the grid search</param>
/// <param name="seed">The random seed (required by the random forest model)</param>
/// <param name="maxDegreeOfParallelism">The maximum allowed number of threads (to parallelize the grid search)</param>
public static RFParameter GridSearch(IRegressionProblemData problemData, Dictionary <string, IEnumerable <double> > parameterRanges, int seed = 12345, int maxDegreeOfParallelism = 1)
{
var setters = parameterRanges.Keys.Select(GenerateSetter).ToList();
var crossProduct = parameterRanges.Values.CartesianProduct();
double bestOutOfBagRmsError = double.MaxValue;
RFParameter bestParameters = new RFParameter();
var locker = new object();
Parallel.ForEach(crossProduct, new ParallelOptions {
MaxDegreeOfParallelism = maxDegreeOfParallelism
}, parameterCombination => {
var parameterValues = parameterCombination.ToList();
var parameters = new RFParameter();
for (int i = 0; i < setters.Count; ++i)
{
setters[i](parameters, parameterValues[i]);
}
double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, parameters.N, parameters.R, parameters.M, seed, out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
lock (locker) {
if (bestOutOfBagRmsError > outOfBagRmsError)
{
bestOutOfBagRmsError = outOfBagRmsError;
bestParameters = (RFParameter)parameters.Clone();
}
}
});
return(bestParameters);
}
public static RandomForestModel CreateRandomForestRegressionModel(IRegressionProblemData problemData, int nTrees,
double r, double m, int seed,
out double rmsError, out double avgRelError, out double outOfBagRmsError, out double outOfBagAvgRelError)
{
return(RandomForestModel.CreateRegressionModel(problemData, nTrees, r, m, seed,
rmsError: out rmsError, avgRelError: out avgRelError, outOfBagRmsError: out outOfBagRmsError, outOfBagAvgRelError: out outOfBagAvgRelError));
}
private static void CrossValidate(IRegressionProblemData problemData, Tuple <IEnumerable <int>, IEnumerable <int> >[] partitions, int nTrees, double r, double m, int seed, out double avgTestMse)
{
avgTestMse = 0;
var ds = problemData.Dataset;
var targetVariable = GetTargetVariableName(problemData);
foreach (var tuple in partitions)
{
double rmsError, avgRelError, outOfBagAvgRelError, outOfBagRmsError;
var trainingRandomForestPartition = tuple.Item1;
var testRandomForestPartition = tuple.Item2;
var model = RandomForestModel.CreateRegressionModel(problemData, trainingRandomForestPartition, nTrees, r, m, seed, out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
var estimatedValues = model.GetEstimatedValues(ds, testRandomForestPartition);
var targetValues = ds.GetDoubleValues(targetVariable, testRandomForestPartition);
OnlineCalculatorError calculatorError;
double mse = OnlineMeanSquaredErrorCalculator.Calculate(estimatedValues, targetValues, out calculatorError);
if (calculatorError != OnlineCalculatorError.None)
{
mse = double.NaN;
}
avgTestMse += mse;
}
avgTestMse /= partitions.Length;
}