private protected override OvaModelParameters CreatePredictor()
{
Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete.");
Host.Assert(_numClass > 1, "Must know the number of classes before creating a predictor.");
Host.Assert(TrainedEnsemble.NumTrees % _numClass == 0, "Number of trees should be a multiple of number of classes.");
var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
IPredictorProducing <float>[] predictors = new IPredictorProducing <float> [_tlcNumClass];
for (int i = 0; i < _tlcNumClass; ++i)
{
var pred = CreateBinaryPredictor(i, innerArgs);
var cali = new PlattCalibrator(Host, -0.5, 0);
predictors[i] = new FeatureWeightsCalibratedPredictor(Host, pred, cali);
}
string obj = (string)GetGbmParameters()["objective"];
if (obj == "multiclass")
{
return(OvaModelParameters.Create(Host, OvaModelParameters.OutputFormula.Softmax, predictors));
}
else
{
return(OvaModelParameters.Create(Host, predictors));
}
}
public override OvaPredictor CreatePredictor()
{
Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete.");
Host.Assert(_numClass > 1, "Must know the number of classes before creating a predictor.");
Host.Assert(TrainedEnsemble.NumTrees % _numClass == 0, "Number of trees should be a multiple of number of classes.");
var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
IPredictorProducing <float>[] predictors = new IPredictorProducing <float> [_tlcNumClass];
for (int i = 0; i < _tlcNumClass; ++i)
{
var pred = CreateBinaryPredictor(i, innerArgs);
var cali = new PlattCalibrator(Host, -0.5, 0);
predictors[i] = new FeatureWeightsCalibratedPredictor(Host, pred, cali);
}
return(OvaPredictor.Create(Host, predictors));
}
