Type Members

1.  case class ClassifierTrainParams(parallelism: String, topK: Option[Int], numIterations: Int, learningRate: Double, numLeaves: Option[Int], maxBin: Option[Int], binSampleCount: Option[Int], baggingFraction: Option[Double], posBaggingFraction: Option[Double], negBaggingFraction: Option[Double], baggingFreq: Option[Int], baggingSeed: Option[Int], earlyStoppingRound: Int, improvementTolerance: Double, featureFraction: Option[Double], maxDepth: Option[Int], minSumHessianInLeaf: Option[Double], numMachines: Int, modelString: Option[String], isUnbalance: Boolean, verbosity: Int, categoricalFeatures: Array[Int], numClass: Int, boostFromAverage: Boolean, boostingType: String, lambdaL1: Option[Double], lambdaL2: Option[Double], isProvideTrainingMetric: Option[Boolean], metric: Option[String], minGainToSplit: Option[Double], maxDeltaStep: Option[Double], maxBinByFeature: Array[Int], minDataInLeaf: Option[Int], featureNames: Array[String], delegate: Option[LightGBMDelegate], dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams) extends TrainParams with Product with Serializable

   Defines the Booster parameters passed to the LightGBM classifier.

2.  case class DartModeParams(dropRate: Double, maxDrop: Int, skipDrop: Double, xgboostDartMode: Boolean, uniformDrop: Boolean) extends Serializable with Product

   Defines the dart mode parameters passed to the LightGBM learners.

3.  case class ExecutionParams(chunkSize: Int, matrixType: String, numThreads: Int, useSingleDatasetMode: Boolean) extends Serializable with Product

   Defines parameters related to lightgbm execution in spark.

   Defines parameters related to lightgbm execution in spark.

   chunkSize

   Advanced parameter to specify the chunk size for copying Java data to native.

   matrixType

   Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.

   numThreads

   The number of threads to run the native lightgbm training with on each worker.

4.  class FObjParam extends ComplexParam[FObjTrait]

   Param for FObjTrait.

   Param for FObjTrait. Needed as spark has explicit params for many different types but not FObjTrait.

5.  trait FObjTrait extends Serializable
6.  trait LightGBMBinParams extends Wrappable

   Defines common parameters across all LightGBM learners related to histogram bin construction.

7.  class LightGBMBoosterParam extends ComplexParam[LightGBMBooster]

   Custom ComplexParam for LightGBMBooster, to make it settable on the LightGBM models.

8.  trait LightGBMDartParams extends Wrappable

   Defines parameters for dart mode across all LightGBM learners.

9.  trait LightGBMExecutionParams extends Wrappable

   Defines common LightGBM execution parameters.

10.  trait LightGBMFractionParams extends Wrappable

    Defines parameters for fraction across all LightGBM learners.

11.  trait LightGBMLearnerParams extends Wrappable

    Defines common parameters across all LightGBM learners related to learning score evolution.

12.  trait LightGBMModelParams extends Wrappable

    Defines parameters for LightGBM models

13.  trait LightGBMObjectiveParams extends Wrappable

    Defines common objective parameters

14.  trait LightGBMParams extends Wrappable with DefaultParamsWritable with HasWeightCol with HasValidationIndicatorCol with HasInitScoreCol with LightGBMExecutionParams with LightGBMSlotParams with LightGBMFractionParams with LightGBMBinParams with LightGBMLearnerParams with LightGBMDartParams with LightGBMPredictionParams with LightGBMObjectiveParams

    Defines common parameters across all LightGBM learners.

15.  trait LightGBMPredictionParams extends Wrappable

    Defines common prediction parameters across LightGBM Ranker, Classifier and Regressor

16.  trait LightGBMSlotParams extends Wrappable

    Defines parameters for slots across all LightGBM learners.

17.  case class ObjectiveParams(objective: String, fobj: Option[FObjTrait]) extends Serializable with Product

    Defines parameters related to the lightgbm objective function.

    Defines parameters related to the lightgbm objective function.

    objective

    The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.

    fobj

    Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).

18.  case class RankerTrainParams(parallelism: String, topK: Option[Int], numIterations: Int, learningRate: Double, numLeaves: Option[Int], maxBin: Option[Int], binSampleCount: Option[Int], baggingFraction: Option[Double], posBaggingFraction: Option[Double], negBaggingFraction: Option[Double], baggingFreq: Option[Int], baggingSeed: Option[Int], earlyStoppingRound: Int, improvementTolerance: Double, featureFraction: Option[Double], maxDepth: Option[Int], minSumHessianInLeaf: Option[Double], numMachines: Int, modelString: Option[String], verbosity: Int, categoricalFeatures: Array[Int], boostingType: String, lambdaL1: Option[Double], lambdaL2: Option[Double], maxPosition: Int, labelGain: Array[Double], isProvideTrainingMetric: Option[Boolean], metric: Option[String], evalAt: Array[Int], minGainToSplit: Option[Double], maxDeltaStep: Option[Double], maxBinByFeature: Array[Int], minDataInLeaf: Option[Int], featureNames: Array[String], delegate: Option[LightGBMDelegate], dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams) extends TrainParams with Product with Serializable

    Defines the Booster parameters passed to the LightGBM ranker.

19.  case class RegressorTrainParams(parallelism: String, topK: Option[Int], numIterations: Int, learningRate: Double, numLeaves: Option[Int], alpha: Double, tweedieVariancePower: Double, maxBin: Option[Int], binSampleCount: Option[Int], baggingFraction: Option[Double], posBaggingFraction: Option[Double], negBaggingFraction: Option[Double], baggingFreq: Option[Int], baggingSeed: Option[Int], earlyStoppingRound: Int, improvementTolerance: Double, featureFraction: Option[Double], maxDepth: Option[Int], minSumHessianInLeaf: Option[Double], numMachines: Int, modelString: Option[String], verbosity: Int, categoricalFeatures: Array[Int], boostFromAverage: Boolean, boostingType: String, lambdaL1: Option[Double], lambdaL2: Option[Double], isProvideTrainingMetric: Option[Boolean], metric: Option[String], minGainToSplit: Option[Double], maxDeltaStep: Option[Double], maxBinByFeature: Array[Int], minDataInLeaf: Option[Int], featureNames: Array[String], delegate: Option[LightGBMDelegate], dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams) extends TrainParams with Product with Serializable

    Defines the Booster parameters passed to the LightGBM regressor.

20.  abstract class TrainParams extends Serializable

    Defines the common Booster parameters passed to the LightGBM learners.