private readonly double _sigmoidParameter; // Parameter for scaling the loss
public ObjectiveImpl(
Dataset trainSet,
bool[] trainSetLabels,
double learningRate,
double shrinkage,
double sigmoidParameter,
bool unbalancedSets,
double maxTreeOutput,
int getDerivativesSampleRate,
bool bestStepRankingRegressionTrees,
int rngSeed,
IParallelTraining parallelTraining)
: base(
trainSet,
learningRate,
shrinkage,
maxTreeOutput,
getDerivativesSampleRate,
bestStepRankingRegressionTrees,
rngSeed)
{
_sigmoidParameter = sigmoidParameter;
_labels = trainSetLabels;
_unbalancedSets = unbalancedSets;
if (_unbalancedSets)
{
BinaryClassificationTest.ComputeExampleCounts(_labels, out _npos, out _nneg);
Contracts.Check(_nneg > 0 && _npos > 0, "Only one class in training set.");
}
_parallelTraining = parallelTraining;
}
public ObjectiveImpl(Dataset trainSet, bool[] trainSetLabels, BinaryClassificationGamTrainer.Arguments args)
: base(
trainSet,
args.LearningRates,
0,
args.MaxOutput,
args.GetDerivativesSampleRate,
false,
args.RngSeed)
{
_labels = trainSetLabels;
_unbalancedSets = args.UnbalancedSets;
if (_unbalancedSets)
{
BinaryClassificationTest.ComputeExampleCounts(_labels, out _npos, out _nneg);
Contracts.Check(_nneg > 0 && _npos > 0, "Only one class in training set.");
}
}
public ObjectiveImpl(Dataset trainSet, bool[] trainSetLabels, Arguments args, IParallelTraining parallelTraining)
: base(
trainSet,
args.LearningRates,
args.Shrinkage,
args.MaxTreeOutput,
args.GetDerivativesSampleRate,
args.BestStepRankingRegressionTrees,
args.RngSeed)
{
_labels = trainSetLabels;
_unbalancedSets = args.UnbalancedSets;
if (_unbalancedSets)
{
BinaryClassificationTest.ComputeExampleCounts(_labels, out _npos, out _nneg);
Contracts.Check(_nneg > 0 && _npos > 0, "Only one class in training set.");
}
_parallelTraining = parallelTraining;
}