private protected override IPredictorWithFeatureWeights <float> TrainModelCore(TrainContext context)
{
Host.CheckValue(context, nameof(context));
var trainData = context.TrainingSet;
ValidData = context.ValidationSet;
TestData = context.TestSet;
using (var ch = Host.Start("Training"))
{
ch.CheckValue(trainData, nameof(trainData));
trainData.CheckBinaryLabel();
trainData.CheckFeatureFloatVector();
trainData.CheckOptFloatWeight();
FeatureCount = trainData.Schema.Feature.Type.ValueCount;
ConvertData(trainData);
TrainCore(ch);
}
// The FastTree binary classification boosting is naturally calibrated to
// output probabilities when transformed using a scaled logistic function,
// so transform the scores using that.
var pred = new FastTreeBinaryModelParameters(Host, TrainedEnsemble, FeatureCount, InnerArgs);
// FastTree's binary classification boosting framework's natural probabilistic interpretation
// is explained in "From RankNet to LambdaRank to LambdaMART: An Overview" by Chris Burges.
// The correctness of this scaling depends upon the gradient calculation in
// BinaryClassificationObjectiveFunction.GetGradientInOneQuery being consistent with the
// description in section 6 of the paper.
var cali = new PlattCalibrator(Host, -1 * _sigmoidParameter, 0);
return(new FeatureWeightsCalibratedPredictor(Host, pred, cali));
}
private static IPredictorProducing <float> Create(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
var predictor = new FastTreeBinaryModelParameters(env, ctx);
ICalibrator calibrator;
ctx.LoadModelOrNull <ICalibrator, SignatureLoadModel>(env, out calibrator, @"Calibrator");
if (calibrator == null)
{
return(predictor);
}
return(new SchemaBindableCalibratedModelParameters <FastTreeBinaryModelParameters, ICalibrator>(env, predictor, calibrator));
}
IPredictor IModelCombiner.CombineModels(IEnumerable <IPredictor> models)
{
_host.CheckValue(models, nameof(models));
var ensemble = new InternalTreeEnsemble();
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 IWeaklyTypedCalibratedModelParameters;
double paramA = 1;
if (calibrated != null)
{
_host.Check(calibrated.WeeklyTypedCalibrator is PlattCalibrator,
"Combining FastTree models can only be done when the models are calibrated with Platt calibrator");
}
predictor = calibrated.WeeklyTypedSubModel;
paramA = -((PlattCalibrator)calibrated.WeeklyTypedCalibrator).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 InternalRegressionTree(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.GetValueCount();
}
else
{
_host.Check((calibrated != null) == binaryClassifier, "Ensemble contains both calibrated and uncalibrated models");
_host.Check(featureCount == tree.InputType.GetValueCount(), "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);
var fastTreeModel = new FastTreeBinaryModelParameters(_host, ensemble, featureCount, null);
return(new FeatureWeightsCalibratedModelParameters <FastTreeBinaryModelParameters, PlattCalibrator>(_host, fastTreeModel, 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();
}
}
