public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { LightGbmRankingTrainer.Options options = TrainerExtensionUtil.CreateLightGbmOptions <LightGbmRankingTrainer.Options, float, RankingPredictionTransformer <LightGbmRankingModelParameters>, LightGbmRankingModelParameters>(sweepParams, columnInfo); options.RowGroupColumnName = columnInfo.GroupIdColumnName; return(mlContext.Ranking.Trainers.LightGbm(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { LightGbmBinaryTrainer.Options options = TrainerExtensionUtil.CreateLightGbmOptions <LightGbmBinaryTrainer.Options, float, BinaryPredictionTransformer <CalibratedModelParametersBase <LightGbmBinaryModelParameters, PlattCalibrator> >, CalibratedModelParametersBase <LightGbmBinaryModelParameters, PlattCalibrator> >(sweepParams, columnInfo); return(mlContext.BinaryClassification.Trainers.LightGbm(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { LightGbmMulticlassTrainer.Options options = TrainerExtensionUtil.CreateLightGbmOptions <LightGbmMulticlassTrainer.Options, VBuffer <float>, MulticlassPredictionTransformer <OneVersusAllModelParameters>, OneVersusAllModelParameters>(sweepParams, columnInfo); return(mlContext.MulticlassClassification.Trainers.LightGbm(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { LightGbmRegressionTrainer.Options options = TrainerExtensionUtil.CreateLightGbmOptions <LightGbmRegressionTrainer.Options, float, RegressionPredictionTransformer <LightGbmRegressionModelParameters>, LightGbmRegressionModelParameters>(sweepParams, columnInfo); return(mlContext.Regression.Trainers.LightGbm(options)); }