public OptimizationAlgorithm(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores)
{
Ensemble = ensemble;
TrainingScores = ConstructScoreTracker("train", trainData, initTrainScores);
TrackedScores = new List <ScoreTracker>();
TrackedScores.Add(TrainingScores);
DropoutRng = new Random();
UseFastTrainingScoresUpdate = true;
}
// 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, TreeEnsemble 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 static string TreeEnsembleToIni(
IHost host, TreeEnsemble ensemble, RoleMappedSchema schema, ICalibrator calibrator,
string trainingParams, bool appendFeatureGain, bool includeZeroGainFeatures)
{
host.CheckValue(ensemble, nameof(ensemble));
host.CheckValue(schema, nameof(schema));
string ensembleIni = ensemble.ToTreeEnsembleIni(new FeaturesToContentMap(schema),
trainingParams, appendFeatureGain, includeZeroGainFeatures);
ensembleIni = AddCalibrationToIni(host, ensembleIni, calibrator);
return(ensembleIni);
}
public bool Compress(IChannel ch, TreeEnsemble ensemble, double[] trainScores, int bestIteration, int maxTreesAfterCompression)
{
LoadTargets(trainScores, bestIteration);
LassoFit fit = GetLassoFit(ch, maxTreesAfterCompression);
int numberOfSolutions = fit.NumberOfLambdas;
int bestSolutionIdx = 0;
ch.Info("Compression R2 values:");
for (int i = 0; i < numberOfSolutions; i++)
{
ch.Info("Solution {0}:\t{1}\t{2}", i + 1, fit.NonZeroWeights[i], fit.Rsquared[i]);
}
bestSolutionIdx = numberOfSolutions - 1;
_compressedEnsemble = GetEnsembleFromSolution(fit, bestSolutionIdx, ensemble);
return(true);
}
private TreeEnsemble GetEnsembleFromSolution(LassoFit fit, int solutionIdx, TreeEnsemble originalEnsemble)
{
TreeEnsemble ensemble = new TreeEnsemble();
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 AcceleratedGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
: base(ensemble, trainData, initTrainScores, gradientWrapper)
{
UseFastTrainingScoresUpdate = false;
}
// REVIEW: When the FastTree appliation is decoupled with tree learner and boosting logic, this class should be removed.
public RandomForestOptimizer(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
: base(ensemble, trainData, initTrainScores, gradientWrapper)
{
_gradientWrapper = gradientWrapper;
}
public IPredictor CombineModels(IEnumerable <IPredictor> models)
{
_host.CheckValue(models, nameof(models));
var ensemble = new TreeEnsemble();
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).Slope;
}
var tree = predictor as TreeEnsembleModelParameters;
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 FastTreeBinaryModelParameters(_host, ensemble, featureCount, null));
}
var cali = new PlattCalibrator(_host, -1, 0);
return(new FeatureWeightsCalibratedPredictor(_host, new FastTreeBinaryModelParameters(_host, ensemble, featureCount, null), cali));
case PredictionKind.Regression:
return(new FastTreeRegressionModelParameters(_host, ensemble, featureCount, null));
case PredictionKind.Ranking:
return(new FastTreeRankingModelParameters(_host, ensemble, featureCount, null));
default:
_host.Assert(false);
throw _host.ExceptNotSupp();
}
}
public GradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
: base(ensemble, trainData, initTrainScores)
{
_gradientWrapper = gradientWrapper;
_treeScores = new List <double[]>();
}
public ConjugateGradientDescent(TreeEnsemble ensemble, Dataset trainData, double[] initTrainScores, IGradientAdjuster gradientWrapper)
: base(ensemble, trainData, initTrainScores, gradientWrapper)
{
_currentDk = new double[trainData.NumDocs];
}
