protected override IPredictorWithFeatureWeights <float> TrainModelCore(TrainContext context)
{
Host.CheckValue(context, nameof(context));
var trainData = context.TrainingSet;
ValidData = context.ValidationSet;
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);
ch.Done();
}
// 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 FastTreeBinaryPredictor(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));
}
public static IPredictorProducing <float> Create(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
var predictor = new FastTreeBinaryPredictor(env, ctx);
ICalibrator calibrator;
ctx.LoadModelOrNull <ICalibrator, SignatureLoadModel>(env, out calibrator, @"Calibrator");
if (calibrator == null)
{
return(predictor);
}
return(new SchemaBindableCalibratedPredictor(env, predictor, calibrator));
}
public override IPredictorWithFeatureWeights <Float> CreatePredictor()
{
Host.Check(TrainedEnsemble != null,
"The predictor cannot be created before training is complete");
// 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 FastTreeBinaryPredictor(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, -2 * Args.LearningRates, 0);
return(new FeatureWeightsCalibratedPredictor(Host, pred, cali));
}