private RandomForestModel(RandomForestModel original, Cloner cloner)
: base(original, cloner) {
randomForest = new alglib.decisionforest();
randomForest.innerobj.bufsize = original.randomForest.innerobj.bufsize;
randomForest.innerobj.nclasses = original.randomForest.innerobj.nclasses;
randomForest.innerobj.ntrees = original.randomForest.innerobj.ntrees;
randomForest.innerobj.nvars = original.randomForest.innerobj.nvars;
// we assume that the trees array (double[]) is immutable in alglib
randomForest.innerobj.trees = original.randomForest.innerobj.trees;
// allowedInputVariables is immutable so we don't need to clone
allowedInputVariables = original.allowedInputVariables;
// clone data which is necessary to rebuild the model
this.seed = original.seed;
this.originalTrainingData = cloner.Clone(original.originalTrainingData);
// classvalues is immutable so we don't need to clone
this.classValues = original.classValues;
this.nTrees = original.nTrees;
this.r = original.r;
this.m = original.m;
}
private RandomForestModel(RandomForestModel original, Cloner cloner)
: base(original, cloner)
{
randomForest = new alglib.decisionforest();
randomForest.innerobj.bufsize = original.randomForest.innerobj.bufsize;
randomForest.innerobj.nclasses = original.randomForest.innerobj.nclasses;
randomForest.innerobj.ntrees = original.randomForest.innerobj.ntrees;
randomForest.innerobj.nvars = original.randomForest.innerobj.nvars;
// we assume that the trees array (double[]) is immutable in alglib
randomForest.innerobj.trees = original.randomForest.innerobj.trees;
// allowedInputVariables is immutable so we don't need to clone
allowedInputVariables = original.allowedInputVariables;
// clone data which is necessary to rebuild the model
this.seed = original.seed;
this.originalTrainingData = cloner.Clone(original.originalTrainingData);
// classvalues is immutable so we don't need to clone
this.classValues = original.classValues;
this.nTrees = original.nTrees;
this.r = original.r;
this.m = original.m;
}
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;
}
public static RandomForestModel CreateRandomForestClassificationModel(IClassificationProblemData problemData, int nTrees, double r, double m, int seed,
out double rmsError, out double relClassificationError, out double outOfBagRmsError, out double outOfBagRelClassificationError)
{
return(RandomForestModel.CreateClassificationModel(problemData, nTrees, r, m, seed,
rmsError: out rmsError, relClassificationError: out relClassificationError, outOfBagRmsError: out outOfBagRmsError, outOfBagRelClassificationError: out outOfBagRelClassificationError));
}
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, out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError));
}