LightGBMBase

abstract def copy(extra: ParamMap): Estimator[TrainedModel]

abstract def fit(dataset: Dataset[_]): TrainedModel

abstract def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): TrainedModel

Gets the trained model given the train parameters and booster.

abstract def getTrainParams(numWorkers: Int, categoricalIndexes: Array[Int], dataset: Dataset[_]): TrainParams

Gets the training parameters.

abstract def stringFromTrainedModel(model: TrainedModel): String

abstract def transformSchema(schema: StructType): StructType

abstract val uid: String

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

def additionalPythonMethods(): String

final def asInstanceOf[T0]: T0

val baggingFraction: DoubleParam

val baggingFreq: IntParam

val baggingSeed: IntParam

val boostFromAverage: BooleanParam

val boostingType: Param[String]

def castColumns(dataset: Dataset[_], trainingCols: Array[(String, DataType)]): DataFrame

val categoricalSlotIndexes: IntArrayParam

val categoricalSlotNames: StringArrayParam

final def clear(param: Param[_]): LightGBMBase.this.type

def clone(): AnyRef

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

val defaultListenPort: IntParam

val earlyStoppingRound: IntParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

val featureFraction: DoubleParam

final val featuresCol: Param[String]

def finalize(): Unit

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[TrainedModel]

def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedModel

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedModel

final def get[T](param: Param[T]): Option[T]

def getBaggingFraction: Double

def getBaggingFreq: Int

def getBaggingSeed: Int

def getBoostFromAverage: Boolean

def getBoostingType: String

def getCategoricalSlotIndexes: Array[Int]

def getCategoricalSlotNames: Array[String]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

def getDefaultListenPort: Int

def getEarlyStoppingRound: Int

def getFeatureFraction: Double

final def getFeaturesCol: String

def getInitScoreCol: String

def getIsProvideTrainingMetric: Boolean

final def getLabelCol: String

def getLambdaL1: Double

def getLambdaL2: Double

def getLearningRate: Double

def getMaxBin: Int

def getMaxDepth: Int

def getMetric: String

def getMinSumHessianInLeaf: Double

def getModelString: String

def getNumBatches: Int

def getNumIterations: Int

def getNumLeaves: Int

def getObjective: String

def getOptGroupCol: Option[String]

Optional group column for Ranking, set to None by default.

final def getOrDefault[T](param: Param[T]): T

def getParallelism: String

def getParam(paramName: String): Param[Any]

def getTimeout: Double

def getTrainingCols(): Array[(String, DataType)]

def getUseBarrierExecutionMode: Boolean

def getValidationIndicatorCol: String

def getVerbosity: Int

def getWeightCol: String

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hashCode(): Int

val initScoreCol: Param[String]

The name of the initial score column

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

def innerTrain(dataset: Dataset[_]): TrainedModel

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

val isProvideTrainingMetric: BooleanParam

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

val lambdaL1: DoubleParam

val lambdaL2: DoubleParam

val learningRate: DoubleParam

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

val maxBin: IntParam

val maxDepth: IntParam

val metric: Param[String]

val minSumHessianInLeaf: DoubleParam

val modelString: Param[String]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numBatches: IntParam

val numIterations: IntParam

val numLeaves: IntParam

val objective: Param[String]

val parallelism: Param[String]

lazy val params: Array[Param[_]]

def prepareDataframe(dataset: Dataset[_], trainingCols: Array[(String, DataType)], numWorkers: Int): DataFrame

def preprocessData(dataset: DataFrame): DataFrame

Allow algorithm specific preprocessing of dataset.

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): LightGBMBase.this.type

final def set(param: String, value: Any): LightGBMBase.this.type

final def set[T](param: Param[T], value: T): LightGBMBase.this.type

def setBaggingFraction(value: Double): LightGBMBase.this.type

def setBaggingFreq(value: Int): LightGBMBase.this.type

def setBaggingSeed(value: Int): LightGBMBase.this.type

def setBoostFromAverage(value: Boolean): LightGBMBase.this.type

def setBoostingType(value: String): LightGBMBase.this.type

def setCategoricalSlotIndexes(value: Array[Int]): LightGBMBase.this.type

def setCategoricalSlotNames(value: Array[String]): LightGBMBase.this.type

final def setDefault(paramPairs: ParamPair[_]*): LightGBMBase.this.type

final def setDefault[T](param: Param[T], value: T): LightGBMBase.this.type

def setDefaultListenPort(value: Int): LightGBMBase.this.type

def setEarlyStoppingRound(value: Int): LightGBMBase.this.type

def setFeatureFraction(value: Double): LightGBMBase.this.type

def setInitScoreCol(value: String): LightGBMBase.this.type

def setIsProvideTrainingMetric(value: Boolean): LightGBMBase.this.type

def setLambdaL1(value: Double): LightGBMBase.this.type

def setLambdaL2(value: Double): LightGBMBase.this.type

def setLearningRate(value: Double): LightGBMBase.this.type

def setMaxBin(value: Int): LightGBMBase.this.type

def setMaxDepth(value: Int): LightGBMBase.this.type

def setMetric(value: String): LightGBMBase.this.type

def setMinSumHessianInLeaf(value: Double): LightGBMBase.this.type

def setModelString(value: String): LightGBMBase.this.type

def setNumBatches(value: Int): LightGBMBase.this.type

def setNumIterations(value: Int): LightGBMBase.this.type

def setNumLeaves(value: Int): LightGBMBase.this.type

def setObjective(value: String): LightGBMBase.this.type

def setParallelism(value: String): LightGBMBase.this.type

def setTimeout(value: Double): LightGBMBase.this.type

def setUseBarrierExecutionMode(value: Boolean): LightGBMBase.this.type

def setValidationIndicatorCol(value: String): LightGBMBase.this.type

def setVerbosity(value: Int): LightGBMBase.this.type

def setWeightCol(value: String): LightGBMBase.this.type

final def synchronized[T0](arg0: ⇒ T0): T0

val timeout: DoubleParam

def toString(): String

def train(dataset: Dataset[_]): TrainedModel

Trains the LightGBM model.

def transformSchema(schema: StructType, logging: Boolean): StructType

val useBarrierExecutionMode: BooleanParam

val validationIndicatorCol: Param[String]

The name of the validation indicator column

val verbosity: IntParam

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

val weightCol: Param[String]

The name of the weight column

def write: MLWriter