public override RegressionTree TrainingIteration(IChannel ch, bool[] activeFeatures)
{
Contracts.CheckValue(ch, nameof(ch));
AgdScoreTracker trainingScores = TrainingScores as AgdScoreTracker;
//First Let's make XK=YK as we want to fit YK and LineSearch YK
// and call base class that uses fits XK (in our case will fir YK thanks to the swap)
var xk = trainingScores.XK;
trainingScores.XK = trainingScores.YK;
trainingScores.YK = null;
//Invoke standard gradient descent on YK rather than XK(Scores)
RegressionTree tree = base.TrainingIteration(ch, activeFeatures);
//Reverse the XK/YK swap
trainingScores.YK = trainingScores.XK;
trainingScores.XK = xk;
if (tree == null)
{
return(null); // No tree was actually learnt. Give up.
}
// ... and update the training scores that we omitted from update
// in AcceleratedGradientDescent.UpdateScores
// Here we could use faster way of comuting train scores taking advantage of scores precompited by LineSearch
// But that would make the code here even more difficult/complex
trainingScores.AddScores(tree, TreeLearner.Partitioning, 1.0);
//Now rescale all previous trees based on ratio of new_desired_tree_scale/previous_tree_scale
for (int t = 0; t < Ensemble.NumTrees - 1; t++)
{
Ensemble.GetTreeAt(t).ScaleOutputsBy(AgdScoreTracker.TreeMultiplier(t, Ensemble.NumTrees) / AgdScoreTracker.TreeMultiplier(t, Ensemble.NumTrees - 1));
}
return(tree);
}
public override void FinalizeLearning(int bestIteration)
{
if (bestIteration != Ensemble.NumTrees)
{
// Restore multiplier for each tree as it was set during bestIteration
for (int t = 0; t < bestIteration; t++)
{
Ensemble.GetTreeAt(t).ScaleOutputsBy(AgdScoreTracker.TreeMultiplier(t, bestIteration) / AgdScoreTracker.TreeMultiplier(t, Ensemble.NumTrees));
}
}
base.FinalizeLearning(bestIteration);
}