public void AdjustTreeOutputs(IChannel ch, RegressionTree tree,
DocumentPartitioning partitioning, ScoreTracker trainingScores)
{
const double epsilon = 1.4e-45;
double multiplier = LearningRate * Shrinkage;
double[] means = null;
if (!BestStepRankingRegressionTrees)
{
means = _parallelTraining.GlobalMean(Dataset, tree, partitioning, Weights, false);
}
for (int l = 0; l < tree.NumLeaves; ++l)
{
double output = tree.GetOutput(l);
if (BestStepRankingRegressionTrees)
{
output *= multiplier;
}
else
{
output = multiplier * (output + epsilon) / (means[l] + epsilon);
}
if (output > MaxTreeOutput)
{
output = MaxTreeOutput;
}
else if (output < -MaxTreeOutput)
{
output = -MaxTreeOutput;
}
tree.SetOutput(l, output);
}
}
public double[] GlobalMean(Dataset dataset, RegressionTree tree, DocumentPartitioning partitioning, double[] weights, bool filterZeroLambdas)
{
double[] means = new double[tree.NumLeaves];
for (int l = 0; l < tree.NumLeaves; ++l)
{
means[l] = partitioning.Mean(weights, dataset.SampleWeights, l, filterZeroLambdas);
}
return(means);
}
public void AdjustTreeOutputs(IChannel ch, RegressionTree tree, DocumentPartitioning partitioning, ScoreTracker trainingScores)
{
double shrinkage = LearningRate * Shrinkage;
for (int l = 0; l < tree.NumLeaves; ++l)
{
double output = tree.GetOutput(l) * shrinkage;
tree.SetOutput(l, output);
}
}
public void AdjustTreeOutputs(IChannel ch, RegressionTree tree, DocumentPartitioning partitioning, ScoreTracker trainingScores)
{
double shrinkage = LearningRate * Shrinkage;
var scores = trainingScores.Scores;
var weights = trainingScores.Dataset.SampleWeights;
// Following equation 18, and line 2c of algorithm 1 in the source paper.
for (int l = 0; l < tree.NumLeaves; ++l)
{
Double num = 0;
Double denom = 0;
if (_index1 == 0)
{
// The index == 1 Poisson case.
foreach (int i in partitioning.DocumentsInLeaf(l))
{
var s = scores[i];
var w = weights == null ? 1 : weights[i];
num += w * _labels[i];
denom += w * Math.Exp(s);
}
}
else
{
// The index in (1,2] case.
foreach (int i in partitioning.DocumentsInLeaf(l))
{
var s = scores[i];
var w = weights == null ? 1 : weights[i];
num += w * _labels[i] * Math.Exp(_index1 * s);
denom += w * Math.Exp(_index2 * s);
}
}
var step = shrinkage * (Math.Log(num) - Math.Log(denom));
if (num == 0 && denom == 0)
{
step = 0;
}
// If we do not clamp, it is entirely possible for num to be 0 (with 0 labels), which
// means that we will have negative infinities in the leaf nodes. This has a number of
// bad negative effects we'd prefer to avoid. Nonetheless, we do give up a substantial
// amount of "gain" for those examples.
if (step < -_maxClamp)
{
step = -_maxClamp;
}
else if (step > _maxClamp)
{
step = _maxClamp;
}
tree.SetOutput(l, step);
}
}
public double[] GlobalMean(Dataset dataset, RegressionTree tree, DocumentPartitioning partitioning, double[] weights, bool filterZeroLambdas)
{
Assert.True(_isInitEnv);
Assert.True(_isInitTreeLearner);
Assert.NotNull(dataset);
Assert.NotNull(tree);
Assert.NotNull(partitioning);
double[] means = new double[tree.NumLeaves];
for (int l = 0; l < tree.NumLeaves; ++l)
{
means[l] = partitioning.Mean(weights, dataset.SampleWeights, l, filterZeroLambdas);
}
return(means);
}
public void AdjustTreeOutputs(IChannel ch, RegressionTree tree, DocumentPartitioning partitioning, ScoreTracker trainingScores)
{
}
