/// <summary>
/// Training algorithm for the single-feature functions f(x)
/// </summary>
/// <param name="ch">The channel to write to</param>
private void TrainMainEffectsModel(IChannel ch)
{
Contracts.AssertValue(ch);
int iterations = Args.NumIterations;
ch.Info("Starting to train ...");
using (var pch = Host.StartProgressChannel("GAM training"))
{
_objectiveFunction = CreateObjectiveFunction();
var sumWeights = HasWeights ? TrainSet.SampleWeights.Sum() : 0;
int iteration = 0;
pch.SetHeader(new ProgressHeader("iterations"), e => e.SetProgress(0, iteration, iterations));
for (int i = iteration; iteration < iterations; iteration++)
{
using (Timer.Time(TimerEvent.Iteration))
{
var gradient = _objectiveFunction.GetGradient(ch, TrainSetScore.Scores);
var sumTargets = gradient.Sum();
SumUpsAcrossFlocks(gradient, sumTargets, sumWeights);
TrainOnEachFeature(gradient, TrainSetScore.Scores, sumTargets, sumWeights, iteration);
UpdateScores(iteration);
}
}
}
CombineGraphs(ch);
}
private void Initialize(IChannel ch)
{
using (Timer.Time(TimerEvent.InitializeTraining))
{
InitializeGamHistograms();
_subGraph = new SubGraph(TrainSet.NumFeatures, Args.NumIterations);
_leafSplitCandidates = new LeastSquaresRegressionTreeLearner.LeafSplitCandidates(TrainSet);
_leafSplitHelper = new LeafSplitHelper(HasWeights);
}
}
private void TrainCore(IChannel ch)
{
Contracts.CheckValue(ch, nameof(ch));
// REVIEW:Get rid of this lock then we completly remove all static classes from Gam such as BlockingThreadPool.
lock (FastTreeShared.TrainLock)
{
using (Timer.Time(TimerEvent.TotalInitialization))
Initialize(ch);
using (Timer.Time(TimerEvent.TotalTrain))
TrainMainEffectsModel(ch);
}
}