Type Members

1.  abstract class BaseTrainParams extends Serializable

   Defines the common Booster parameters passed to the LightGBM learners.

2.  case class CategoricalParams(minDataPerGroup: Option[Int], maxCatThreshold: Option[Int], catl2: Option[Double], catSmooth: Option[Double], maxCatToOneHot: Option[Int]) extends ParamGroup with Product with Serializable

   Defines parameters related to categorical features for lightgbm.

   Defines parameters related to categorical features for lightgbm.

   minDataPerGroup

   minimal number of data per categorical group.

   maxCatThreshold

   limit number of split points considered for categorical features

   catl2

   L2 regularization in categorical split.

   catSmooth

   this can reduce the effect of noises in categorical features, especially for categories with few data.

   maxCatToOneHot

   when number of categories of one feature smaller than or equal to this, one-vs-other split algorithm will be used.

3.  case class ClassifierTrainParams(passThroughArgs: Option[String], isUnbalance: Boolean, boostFromAverage: Boolean, isProvideTrainingMetric: Option[Boolean], delegate: Option[LightGBMDelegate], generalParams: GeneralParams, datasetParams: DatasetParams, dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams, seedParams: SeedParams, categoricalParams: CategoricalParams, numClass: Int = 1) extends BaseTrainParams with Product with Serializable

   Defines the Booster parameters passed to the LightGBM classifier.

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

   Defines the dart mode parameters passed to the LightGBM learners.

5.  case class DatasetParams(isEnableSparse: Option[Boolean], useMissing: Option[Boolean], zeroAsMissing: Option[Boolean]) extends ParamGroup with Product with Serializable

   Defines the Dataset parameters passed to the LightGBM classifier.

   Defines the Dataset parameters passed to the LightGBM classifier.

   isEnableSparse

   Used to enable/disable sparse optimization.

   useMissing

   Set this to false to disable the special handle of missing value.

   zeroAsMissing

   Set to true to treat all zero as missing values (including the unshown values in LibSVM/sparse matrices) Set to false to use na for representing missing values.

6.  case class ExecutionParams(chunkSize: Int, matrixType: String, numThreads: Int, dataTransferMode: String, samplingMode: String, samplingSetSize: Int, microBatchSize: Int, useSingleDatasetMode: Boolean, maxStreamingOMPThreads: Int) extends ParamGroup with Product with Serializable

   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.

   dataTransferMode

   How to transfer data to LightGBM to begin the processing.

   samplingMode

   How to sample data.

   samplingSetSize

   The size of the subset if sampling only a subset.

   microBatchSize

   The number of elements in a streaming micro-batch.

   useSingleDatasetMode

   Whether to create only 1 LightGBM Dataset on each worker.

   maxStreamingOMPThreads

   Maximum number of streaming mode OpenMP threads per Spark Task thread.

7.  class FObjParam extends ComplexParam[FObjTrait]

   Param for FObjTrait.

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

8.  trait FObjTrait extends Serializable
9.  case class GeneralParams(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], featureFractionByNode: Option[Double], maxDepth: Option[Int], minSumHessianInLeaf: Option[Double], numMachines: Int, modelString: Option[String], categoricalFeatures: Array[Int], verbosity: Int, boostingType: String, lambdaL1: Option[Double], lambdaL2: Option[Double], metric: Option[String], minGainToSplit: Option[Double], maxDeltaStep: Option[Double], maxBinByFeature: Array[Int], minDataPerBin: Option[Int], minDataInLeaf: Option[Int], topRate: Option[Double], otherRate: Option[Double], monotoneConstraints: Array[Int], monotoneConstraintsMethod: Option[String], monotonePenalty: Option[Double], featureNames: Array[String]) extends ParamGroup with Product with Serializable

   Defines the general Booster parameters passed to the LightGBM library.

10.  trait LightGBMBinParams extends Wrappable

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

11.  class LightGBMBoosterParam extends ComplexParam[LightGBMBooster] with WrappableParam[LightGBMBooster]

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

12.  trait LightGBMCategoricalParams extends Wrappable

    Defines common parameters across all LightGBM learners related to categorical variable treatment.

13.  trait LightGBMDartParams extends Wrappable

    Defines parameters for dart mode across all LightGBM learners.

14.  trait LightGBMDatasetParams extends Wrappable

    Defines common parameters across all LightGBM learners related to dataset handling.

15.  trait LightGBMExecutionParams extends Wrappable

    Defines common LightGBM execution parameters.

16.  trait LightGBMFractionParams extends Wrappable

    Defines parameters for fraction across all LightGBM learners.

17.  trait LightGBMLearnerParams extends Wrappable

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

18.  trait LightGBMModelParams extends Wrappable

    Defines parameters for LightGBM models

19.  trait LightGBMObjectiveParams extends Wrappable

    Defines common objective parameters

20.  trait LightGBMParams extends Wrappable with DefaultParamsWritable with HasWeightCol with HasValidationIndicatorCol with HasInitScoreCol with LightGBMExecutionParams with LightGBMSlotParams with LightGBMFractionParams with LightGBMBinParams with LightGBMLearnerParams with LightGBMDatasetParams with LightGBMDartParams with LightGBMPredictionParams with LightGBMObjectiveParams with LightGBMSeedParams with LightGBMCategoricalParams

    Defines common parameters across all LightGBM learners.

21.  trait LightGBMPredictionParams extends Wrappable

    Defines common prediction parameters across LightGBM Ranker, Classifier and Regressor

22.  trait LightGBMSeedParams extends Wrappable

    Defines common parameters related to seed and determinism

23.  trait LightGBMSlotParams extends Wrappable

    Defines parameters for slots across all LightGBM learners.

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

    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).

25.  case class RankerTrainParams(passThroughArgs: Option[String], maxPosition: Int, labelGain: Array[Double], evalAt: Array[Int], isProvideTrainingMetric: Option[Boolean], delegate: Option[LightGBMDelegate], generalParams: GeneralParams, datasetParams: DatasetParams, dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams, seedParams: SeedParams, categoricalParams: CategoricalParams) extends BaseTrainParams with Product with Serializable

    Defines the Booster parameters passed to the LightGBM ranker.

26.  case class RegressorTrainParams(passThroughArgs: Option[String], alpha: Double, tweedieVariancePower: Double, boostFromAverage: Boolean, isProvideTrainingMetric: Option[Boolean], delegate: Option[LightGBMDelegate], generalParams: GeneralParams, datasetParams: DatasetParams, dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams, seedParams: SeedParams, categoricalParams: CategoricalParams) extends BaseTrainParams with Product with Serializable

    Defines the Booster parameters passed to the LightGBM regressor.

27.  case class SeedParams(seed: Option[Int], deterministic: Option[Boolean], baggingSeed: Option[Int], featureFractionSeed: Option[Int], extraSeed: Option[Int], dropSeed: Option[Int], dataRandomSeed: Option[Int], objectiveSeed: Option[Int], boostingType: String, objective: String) extends ParamGroup with Product with Serializable

    Defines parameters related to seed and determinism for lightgbm.

    Defines parameters related to seed and determinism for lightgbm.

    seed

    Main seed, used to generate other seeds.

    deterministic

    Setting this to true should ensure stable results when using the same data and the same parameters.

    baggingSeed

    Bagging seed.

    featureFractionSeed

    Feature fraction seed.

    extraSeed

    Random seed for selecting threshold when extra_trees is true.

    dropSeed

    Random seed to choose dropping models. Only used in dart.

    dataRandomSeed

    Random seed for sampling data to construct histogram bins.

    objectiveSeed

    Random seed for objectives, if random process is needed. Currently used only for rank_xendcg objective.

    boostingType

    Boosting type, used to determine if drop seed should be set.

    objective

    Objective, used to determine if objective seed should be set.