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;
}
protected override void DefinePruningTest()
{
var validTest = new BinaryClassificationTest(ValidSetScore,
ConvertTargetsToBool(ValidSet.Targets), _sigmoidParameter);
// As per FastTreeClassification.ConstructOptimizationAlgorithm()
PruningLossIndex = Args.UnbalancedSets ? 3 /*Unbalanced sets loss*/ : 1 /*normal loss*/;
PruningTest = new TestHistory(validTest, PruningLossIndex);
}
protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(IChannel ch)
{
OptimizationAlgorithm optimizationAlgorithm = base.ConstructOptimizationAlgorithm(ch);
if (Args.UseLineSearch)
{
var lossCalculator = new BinaryClassificationTest(optimizationAlgorithm.TrainingScores, _trainSetLabels, _sigmoidParameter);
// REVIEW: we should makeloss indices an enum in BinaryClassificationTest
optimizationAlgorithm.AdjustTreeOutputsOverride = new LineSearch(lossCalculator, Args.UnbalancedSets ? 3 /*Unbalanced sets loss*/ : 1 /*normal loss*/, Args.NumPostBracketSteps, Args.MinStepSize);
}
return(optimizationAlgorithm);
}
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;
}
protected override void InitializeTests()
{
//Always compute training L1/L2 errors
TrainTest = new BinaryClassificationTest(ConstructScoreTracker(TrainSet), _trainSetLabels, _sigmoidParameter);
Tests.Add(TrainTest);
if (ValidSet != null)
{
ValidTest = new BinaryClassificationTest(ConstructScoreTracker(ValidSet),
GetClassificationLabelsFromRatings(ValidSet).ToArray(), _sigmoidParameter);
Tests.Add(ValidTest);
}
//If external label is missing use Rating column for L1/L2 error
//The values may not make much sense if regression value is not an actual label value
if (TestSets != null)
{
for (int t = 0; t < TestSets.Length; ++t)
{
bool[] labels = GetClassificationLabelsFromRatings(TestSets[t]).ToArray();
Tests.Add(new BinaryClassificationTest(ConstructScoreTracker(TestSets[t]), labels, _sigmoidParameter));
}
}
if (Args.EnablePruning && ValidSet != null)
{
if (!Args.UseTolerantPruning)
{
//use simple early stopping condition
PruningTest = new TestHistory(ValidTest, 0);
}
else
{
//use tollerant stopping condition
PruningTest = new TestWindowWithTolerance(ValidTest, 0, Args.PruningWindowSize, Args.PruningThreshold);
}
}
}
