LightGBMClassifier

Instance Constructors

new LightGBMClassifier()
new LightGBMClassifier(uid: String)

uid
The unique ID.

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val baggingFraction: DoubleParam

Definition Classes
LightGBMParams
val baggingFreq: IntParam

Definition Classes
LightGBMParams
val baggingSeed: IntParam

Definition Classes
LightGBMParams
val boostFromAverage: BooleanParam

Definition Classes
LightGBMParams
val boostingType: Param[String]

Definition Classes
LightGBMParams
val categoricalSlotIndexes: IntArrayParam

Definition Classes
LightGBMParams
val categoricalSlotNames: StringArrayParam

Definition Classes
LightGBMParams
final def clear(param: Param[_]): LightGBMClassifier.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(extra: ParamMap): LightGBMClassifier

Definition Classes
LightGBMClassifier → Predictor → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
val defaultListenPort: IntParam

Definition Classes
LightGBMExecutionParams
val earlyStoppingRound: IntParam

Definition Classes
LightGBMParams
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

Attributes
protected
Definition Classes
Classifier
def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

Attributes
protected
Definition Classes
Predictor
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
val featureFraction: DoubleParam

Definition Classes
LightGBMParams
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): LightGBMClassificationModel

Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[LightGBMClassificationModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): LightGBMClassificationModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): LightGBMClassificationModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
val generateMissingLabels: BooleanParam
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
def getBaggingFraction: Double

Definition Classes
LightGBMParams
def getBaggingFreq: Int

Definition Classes
LightGBMParams
def getBaggingSeed: Int

Definition Classes
LightGBMParams
def getBoostFromAverage: Boolean

Definition Classes
LightGBMParams
def getBoostingType: String

Definition Classes
LightGBMParams
def getCategoricalSlotIndexes: Array[Int]

Definition Classes
LightGBMParams
def getCategoricalSlotNames: Array[String]

Definition Classes
LightGBMParams
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getDefaultListenPort: Int

Definition Classes
LightGBMExecutionParams
def getEarlyStoppingRound: Int

Definition Classes
LightGBMParams
def getFeatureFraction: Double

Definition Classes
LightGBMParams
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
def getGenerateMissingLabels: Boolean
def getInitScoreCol: String

Definition Classes
HasInitScoreCol
def getIsProvideTrainingMetric: Boolean

Definition Classes
LightGBMParams
def getIsUnbalance: Boolean
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLambdaL1: Double

Definition Classes
LightGBMParams
def getLambdaL2: Double

Definition Classes
LightGBMParams
def getLearningRate: Double

Definition Classes
LightGBMParams
def getMaxBin: Int

Definition Classes
LightGBMParams
def getMaxDepth: Int

Definition Classes
LightGBMParams
def getMinSumHessianInLeaf: Double

Definition Classes
LightGBMParams
def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMClassificationModel

Gets the trained model given the train parameters and booster.
Gets the trained model given the train parameters and booster.
returns
trained model.

Definition Classes
LightGBMClassifier → LightGBMBase
def getModelString: String

Definition Classes
LightGBMParams
def getNumBatches: Int

Definition Classes
LightGBMParams
def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

Attributes
protected
Definition Classes
Classifier
def getNumIterations: Int

Definition Classes
LightGBMParams
def getNumLeaves: Int

Definition Classes
LightGBMParams
def getObjective: String

Definition Classes
LightGBMParams
def getOptGroupCol: Option[String]

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

Attributes
protected
Definition Classes
LightGBMBase
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParallelism: String

Definition Classes
LightGBMExecutionParams
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
def getThresholds: Array[Double]

Definition Classes
HasThresholds
def getTimeout: Double

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

Gets the training parameters.
Gets the training parameters.
returns
train parameters.

Definition Classes
LightGBMClassifier → LightGBMBase
def getUseBarrierExecutionMode: Boolean

Definition Classes
LightGBMExecutionParams
def getValidationIndicatorCol: String

Definition Classes
HasValidationIndicatorCol
def getVerbosity: Int

Definition Classes
LightGBMParams
def getWeightCol: String

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

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
val initScoreCol: Param[String]

The name of the initial score column
The name of the initial score column

Definition Classes
HasInitScoreCol
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
def innerTrain(dataset: Dataset[_]): LightGBMClassificationModel

Attributes
protected
Definition Classes
LightGBMBase
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val isProvideTrainingMetric: BooleanParam

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

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
val isUnbalance: BooleanParam
final val labelCol: Param[String]

Definition Classes
HasLabelCol
val lambdaL1: DoubleParam

Definition Classes
LightGBMParams
val lambdaL2: DoubleParam

Definition Classes
LightGBMParams
val learningRate: DoubleParam

Definition Classes
LightGBMParams
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
val maxBin: IntParam

Definition Classes
LightGBMParams
val maxDepth: IntParam

Definition Classes
LightGBMParams
val minSumHessianInLeaf: DoubleParam

Definition Classes
LightGBMParams
val modelString: Param[String]

Definition Classes
LightGBMParams
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numBatches: IntParam

Definition Classes
LightGBMParams
val numIterations: IntParam

Definition Classes
LightGBMParams
val numLeaves: IntParam

Definition Classes
LightGBMParams
val objective: Param[String]

Definition Classes
LightGBMParams
val parallelism: Param[String]

Definition Classes
LightGBMExecutionParams
lazy val params: Array[Param[_]]

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
def preprocessData(dataset: DataFrame): DataFrame

Allow algorithm specific preprocessing of dataset.
Allow algorithm specific preprocessing of dataset.
dataset
The dataset to preprocess prior to training.
returns
The preprocessed data.

Attributes
protected
Definition Classes
LightGBMBase
final val probabilityCol: Param[String]

Definition Classes
HasProbabilityCol
final val rawPredictionCol: Param[String]

Definition Classes
HasRawPredictionCol
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): LightGBMClassifier.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): LightGBMClassifier.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): LightGBMClassifier.this.type

Definition Classes
Params
def setBaggingFraction(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setBaggingFreq(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setBaggingSeed(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setBoostFromAverage(value: Boolean): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setBoostingType(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setCategoricalSlotIndexes(value: Array[Int]): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setCategoricalSlotNames(value: Array[String]): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
final def setDefault(paramPairs: ParamPair[_]*): LightGBMClassifier.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): LightGBMClassifier.this.type

Attributes
protected
Definition Classes
Params
def setDefaultListenPort(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMExecutionParams
def setEarlyStoppingRound(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setFeatureFraction(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setFeaturesCol(value: String): LightGBMClassifier

Definition Classes
Predictor
def setGenerateMissingLabels(value: Boolean): LightGBMClassifier.this.type
def setInitScoreCol(value: String): LightGBMClassifier.this.type

Definition Classes
HasInitScoreCol
def setIsUnbalance(value: Boolean): LightGBMClassifier.this.type
def setLabelCol(value: String): LightGBMClassifier

Definition Classes
Predictor
def setLambdaL1(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setLambdaL2(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setLearningRate(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setMaxBin(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setMaxDepth(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setMinSumHessianInLeaf(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setModelString(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setNumBatches(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setNumIterations(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setNumLeaves(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setObjective(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setParallelism(value: String): LightGBMClassifier.this.type

Definition Classes
LightGBMExecutionParams
def setPredictionCol(value: String): LightGBMClassifier

Definition Classes
Predictor
def setProbabilityCol(value: String): LightGBMClassifier

Definition Classes
ProbabilisticClassifier
def setRawPredictionCol(value: String): LightGBMClassifier

Definition Classes
Classifier
def setThresholds(value: Array[Double]): LightGBMClassifier

Definition Classes
ProbabilisticClassifier
def setTimeout(value: Double): LightGBMClassifier.this.type

Definition Classes
LightGBMExecutionParams
def setUseBarrierExecutionMode(value: Boolean): LightGBMClassifier.this.type

Definition Classes
LightGBMExecutionParams
def setValidationIndicatorCol(value: String): LightGBMClassifier.this.type

Definition Classes
HasValidationIndicatorCol
def setVerbosity(value: Int): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def setWeightCol(value: String): LightGBMClassifier.this.type

Definition Classes
HasWeightCol
def setisProvideTrainingMetric(value: Boolean): LightGBMClassifier.this.type

Definition Classes
LightGBMParams
def stringFromTrainedModel(model: LightGBMClassificationModel): String

Definition Classes
LightGBMClassifier → LightGBMBase
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final val thresholds: DoubleArrayParam

Definition Classes
HasThresholds
val timeout: DoubleParam

Definition Classes
LightGBMExecutionParams
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def train(dataset: Dataset[_]): LightGBMClassificationModel

Trains the LightGBM model.
Trains the LightGBM model.
dataset
The input dataset to train.
returns
The trained model.

Attributes
protected
Definition Classes
LightGBMBase
def transformSchema(schema: StructType): StructType

Definition Classes
Predictor → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

The unique ID.
The unique ID.

Definition Classes
LightGBMClassifier → Identifiable
val useBarrierExecutionMode: BooleanParam

Definition Classes
LightGBMExecutionParams
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Attributes
protected
Definition Classes
ProbabilisticClassifierParams → ClassifierParams → PredictorParams
val validationIndicatorCol: Param[String]

The name of the validation indicator column
The name of the validation indicator column

Definition Classes
HasValidationIndicatorCol
val verbosity: IntParam

Definition Classes
LightGBMParams
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
val weightCol: Param[String]

The name of the weight column
The name of the weight column

Definition Classes
HasWeightCol
def write: MLWriter

Definition Classes
DefaultParamsWritable → MLWritable

Inherited from LightGBMBase[LightGBMClassificationModel]

Inherited from LightGBMParams

Inherited from LightGBMExecutionParams

Inherited from HasInitScoreCol

Inherited from HasValidationIndicatorCol

Inherited from HasWeightCol

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Wrappable

Inherited from ProbabilisticClassifier[Vector, LightGBMClassifier, LightGBMClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, LightGBMClassifier, LightGBMClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, LightGBMClassifier, LightGBMClassificationModel]

Related Docs: object LightGBMClassifier | package lightgbm

class LightGBMClassifier extends ProbabilisticClassifier[Vector, LightGBMClassifier, LightGBMClassificationModel] with LightGBMBase[LightGBMClassificationModel]

Instance Constructors

new LightGBMClassifier()

new LightGBMClassifier(uid: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val baggingFraction: DoubleParam

val baggingFreq: IntParam

val baggingSeed: IntParam

val boostFromAverage: BooleanParam

val boostingType: Param[String]

val categoricalSlotIndexes: IntArrayParam

val categoricalSlotNames: StringArrayParam

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

def clone(): AnyRef

def copy(extra: ParamMap): LightGBMClassifier

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

def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

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[_]): LightGBMClassificationModel

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

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

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

val generateMissingLabels: BooleanParam

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 getGenerateMissingLabels: Boolean

def getInitScoreCol: String

def getIsProvideTrainingMetric: Boolean

def getIsUnbalance: Boolean

final def getLabelCol: String

def getLambdaL1: Double

def getLambdaL2: Double

def getLearningRate: Double

def getMaxBin: Int

def getMaxDepth: Int

def getMinSumHessianInLeaf: Double

def getModel(trainParams: TrainParams, lightGBMBooster: LightGBMBooster): LightGBMClassificationModel

def getModelString: String

def getNumBatches: Int

def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

def getNumIterations: Int

def getNumLeaves: Int

def getObjective: String

def getOptGroupCol: Option[String]

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

def getParallelism: String

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

final def getPredictionCol: String

final def getProbabilityCol: String