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)); }