internal override InternalRegressionTree TrainingIteration(IChannel ch, bool[] activeFeatures)
{
Contracts.CheckValue(ch, nameof(ch));
// Fit a regression tree to the gradient using least squares.
InternalRegressionTree tree = TreeLearner.FitTargets(ch, activeFeatures, AdjustTargetsAndSetWeights(ch));
if (tree == null)
{
return(null); // Could not learn a tree. Exit.
}
// Adjust output values of tree by performing a Newton step.
// REVIEW: This should be part of OptimizingAlgorithm.
using (Timer.Time(TimerEvent.TreeLearnerAdjustTreeOutputs))
{
double[] backupScores = null;
// when doing dropouts we need to replace the TrainingScores with the scores without the dropped trees
if (DropoutRate > 0)
{
backupScores = TrainingScores.Scores;
TrainingScores.Scores = _scores;
}
if (AdjustTreeOutputsOverride != null)
{
AdjustTreeOutputsOverride.AdjustTreeOutputs(ch, tree, TreeLearner.Partitioning, TrainingScores);
}
else if (ObjectiveFunction is IStepSearch)
{
(ObjectiveFunction as IStepSearch).AdjustTreeOutputs(ch, tree, TreeLearner.Partitioning, TrainingScores);
}
else
{
throw ch.Except("No AdjustTreeOutputs defined. Objective function should define IStepSearch or AdjustTreeOutputsOverride should be set");
}
if (DropoutRate > 0)
{
// Returning the original scores.
TrainingScores.Scores = backupScores;
}
}
if (Smoothing != 0.0)
{
SmoothTree(tree, Smoothing);
UseFastTrainingScoresUpdate = false;
}
if (DropoutRate > 0)
{
// Don't do shrinkage if you do dropouts.
double scaling = (1.0 / (1.0 + _numberOfDroppedTrees));
tree.ScaleOutputsBy(scaling);
_treeScores.Add(tree.GetOutputs(TrainingScores.Dataset));
}
UpdateAllScores(ch, tree);
Ensemble.AddTree(tree);
return(tree);
}
public override RegressionTree TrainingIteration(IChannel ch, bool[] activeFeatures)
{
Contracts.CheckValue(ch, nameof(ch));
double[] sampleWeights = null;
double[] targets = GetGradient(ch);
double[] weightedTargets = _gradientWrapper.AdjustTargetAndSetWeights(targets, ObjectiveFunction, out sampleWeights);
RegressionTree tree = ((RandomForestLeastSquaresTreeLearner)TreeLearner).FitTargets(ch, activeFeatures, weightedTargets,
targets, sampleWeights);
if (tree != null)
{
Ensemble.AddTree(tree);
}
return(tree);
}
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);
}
public IPredictor CombineModels(IEnumerable<IPredictor> models)
{
_host.CheckValue(models, nameof(models));
var ensemble = new Ensemble();
int modelCount = 0;
int featureCount = -1;
bool binaryClassifier = false;
foreach (var model in models)
{
modelCount++;
var predictor = model;
_host.CheckValue(predictor, nameof(models), "One of the models is null");
var calibrated = predictor as CalibratedPredictorBase;
double paramA = 1;
if (calibrated != null)
{
_host.Check(calibrated.Calibrator is PlattCalibrator,
"Combining FastTree models can only be done when the models are calibrated with Platt calibrator");
predictor = calibrated.SubPredictor;
paramA = -(calibrated.Calibrator as PlattCalibrator).ParamA;
}
var tree = predictor as FastTreePredictionWrapper;
if (tree == null)
throw _host.Except("Model is not a tree ensemble");
foreach (var t in tree.TrainedEnsemble.Trees)
{
var bytes = new byte[t.SizeInBytes()];
int position = -1;
t.ToByteArray(bytes, ref position);
position = -1;
var tNew = new RegressionTree(bytes, ref position);
if (paramA != 1)
{
for (int i = 0; i < tNew.NumLeaves; i++)
tNew.SetOutput(i, tNew.LeafValues[i] * paramA);
}
ensemble.AddTree(tNew);
}
if (modelCount == 1)
{
binaryClassifier = calibrated != null;
featureCount = tree.InputType.ValueCount;
}
else
{
_host.Check((calibrated != null) == binaryClassifier, "Ensemble contains both calibrated and uncalibrated models");
_host.Check(featureCount == tree.InputType.ValueCount, "Found models with different number of features");
}
}
var scale = 1 / (double)modelCount;
foreach (var t in ensemble.Trees)
{
for (int i = 0; i < t.NumLeaves; i++)
t.SetOutput(i, t.LeafValues[i] * scale);
}
switch (_kind)
{
case PredictionKind.BinaryClassification:
if (!binaryClassifier)
return new FastTreeBinaryPredictor(_host, ensemble, featureCount, null);
var cali = new PlattCalibrator(_host, -1, 0);
return new FeatureWeightsCalibratedPredictor(_host, new FastTreeBinaryPredictor(_host, ensemble, featureCount, null), cali);
case PredictionKind.Regression:
return new FastTreeRegressionPredictor(_host, ensemble, featureCount, null);
case PredictionKind.Ranking:
return new FastTreeRankingPredictor(_host, ensemble, featureCount, null);
default:
_host.Assert(false);
throw _host.ExceptNotSupp();
}
}
