Packages

abstract class Ranker[FeaturesType, Learner <: Ranker[FeaturesType, Learner, M], M <: RankerModel[FeaturesType, M]] extends Predictor[FeaturesType, Learner, M] with PredictorParams with HasGroupCol

Ranker base class

FeaturesType

Type of input features. E.g., org.apache.spark.mllib.linalg.Vector

Learner

Concrete Estimator type

M

Concrete Model type

Linear Supertypes
HasGroupCol, Predictor[FeaturesType, Learner, M], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[M], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
LightGBMRanker
Ordering
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  2. By Inheritance
Inherited
  1. Ranker
  2. HasGroupCol
  3. Predictor
  4. PredictorParams
  5. HasPredictionCol
  6. HasFeaturesCol
  7. HasLabelCol
  8. Estimator
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new Ranker()

Abstract Value Members

  1. abstract def copy(extra: ParamMap): Learner
    Definition Classes
    Predictor → Estimator → PipelineStage → Params
  2. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def clear(param: Param[_]): Ranker.this.type
    Definition Classes
    Params
  2. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  3. def explainParams(): String
    Definition Classes
    Params
  4. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  5. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  6. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  7. def fit(dataset: Dataset[_]): M
    Definition Classes
    Predictor → Estimator
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[M]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): M
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  13. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  14. def getGroupCol: String

    Definition Classes
    HasGroupCol
  15. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  16. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  17. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  18. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  19. val groupCol: Param[String]

    The name of the group column

    The name of the group column

    Definition Classes
    HasGroupCol
  20. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  21. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  22. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  23. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  24. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  25. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  26. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  27. final def set[T](param: Param[T], value: T): Ranker.this.type
    Definition Classes
    Params
  28. def setFeaturesCol(value: String): Learner
    Definition Classes
    Predictor
  29. def setGroupCol(value: String): Ranker.this.type

    Definition Classes
    HasGroupCol
  30. def setLabelCol(value: String): Learner
    Definition Classes
    Predictor
  31. def setPredictionCol(value: String): Learner
    Definition Classes
    Predictor
  32. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  33. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage