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);
}
// Divides output values of leaves to bag count.
// This brings back the final scores generated by model on a same
// range as when we didn't use bagging
public void ScaleEnsembleLeaves(int numTrees, int bagSize, Ensemble ensemble)
{
int bagCount = GetBagCount(numTrees, bagSize);
for (int t = 0; t < ensemble.NumTrees; t++)
{
RegressionTree tree = ensemble.GetTreeAt(t);
tree.ScaleOutputsBy(1.0 / bagCount);
}
}
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);
}
private protected virtual double[] GetGradient(IChannel ch)
{
Contracts.AssertValue(ch);
if (DropoutRate > 0)
{
if (_droppedScores == null)
{
_droppedScores = new double[TrainingScores.Scores.Length];
}
else
{
Array.Clear(_droppedScores, 0, _droppedScores.Length);
}
if (_scores == null)
{
_scores = new double[TrainingScores.Scores.Length];
}
int numberOfTrees = Ensemble.NumTrees;
int[] droppedTrees =
Enumerable.Range(0, numberOfTrees).Where(t => (DropoutRng.NextDouble() < DropoutRate)).ToArray();
_numberOfDroppedTrees = droppedTrees.Length;
if ((_numberOfDroppedTrees == 0) && (numberOfTrees > 0))
{
droppedTrees = new int[] { DropoutRng.Next(numberOfTrees) };
// force at least a single tree to be dropped
_numberOfDroppedTrees = droppedTrees.Length;
}
ch.Trace("dropout: Dropping {0} trees of {1} for rate {2}",
_numberOfDroppedTrees, numberOfTrees, DropoutRate);
foreach (int i in droppedTrees)
{
double[] s = _treeScores[i];
for (int j = 0; j < _droppedScores.Length; j++)
{
_droppedScores[j] += s[j]; // summing up the weights of the dropped tree
s[j] *= _numberOfDroppedTrees / (1.0 + _numberOfDroppedTrees); // rescaling the dropped tree
}
Ensemble.GetTreeAt(i).ScaleOutputsBy(_numberOfDroppedTrees / (1.0 + _numberOfDroppedTrees));
}
for (int j = 0; j < _scores.Length; j++)
{
_scores[j] = TrainingScores.Scores[j] - _droppedScores[j];
TrainingScores.Scores[j] -= _droppedScores[j] / (1.0 + _numberOfDroppedTrees);
}
return(ObjectiveFunction.GetGradient(ch, _scores));
}
else
{
return(ObjectiveFunction.GetGradient(ch, TrainingScores.Scores));
}
}
private Ensemble GetEnsembleFromSolution(LassoFit fit, int solutionIdx, Ensemble originalEnsemble)
{
Ensemble ensemble = new Ensemble();
int weightsCount = fit.NumberOfWeights[solutionIdx];
for (int i = 0; i < weightsCount; i++)
{
double weight = fit.CompressedWeights[solutionIdx][i];
if (weight != 0)
{
RegressionTree tree = originalEnsemble.GetTreeAt(fit.Indices[i]);
tree.Weight = weight;
ensemble.AddTree(tree);
}
}
ensemble.Bias = fit.Intercepts[solutionIdx];
return(ensemble);
}
