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);
}
protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(IChannel ch)
{
OptimizationAlgorithm optimizationAlgorithm = base.ConstructOptimizationAlgorithm(ch);
if (Args.UseLineSearch)
{
var lossCalculator = new RegressionTest(optimizationAlgorithm.TrainingScores);
// REVIEW: We should make loss indices an enum in BinaryClassificationTest.
optimizationAlgorithm.AdjustTreeOutputsOverride = new LineSearch(lossCalculator, 1 /*L2 error*/, Args.NumPostBracketSteps, Args.MinStepSize);
}
return(optimizationAlgorithm);
}
private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(IChannel ch)
{
OptimizationAlgorithm optimizationAlgorithm = base.ConstructOptimizationAlgorithm(ch);
if (FastTreeTrainerOptions.UseLineSearch)
{
var lossCalculator = new RegressionTest(optimizationAlgorithm.TrainingScores);
// REVIEW: We should make loss indices an enum in BinaryClassificationTest.
// REVIEW: Nope, subcomponent.
optimizationAlgorithm.AdjustTreeOutputsOverride = new LineSearch(lossCalculator, 1 /*L2 error*/, FastTreeTrainerOptions.MaximumNumberOfLineSearchSteps, FastTreeTrainerOptions.MinimumStepSize);
}
return(optimizationAlgorithm);
}
