RankingTrainValidationSplit

Instance Constructors

new RankingTrainValidationSplit()
new RankingTrainValidationSplit(uid: String)

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

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val collectSubMetrics: BooleanParam
final val collectSubModels: BooleanParam

Definition Classes
HasCollectSubModels
def copy(extra: ParamMap): RankingTrainValidationSplit

Definition Classes
RankingTrainValidationSplit → 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
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
val estimator: EstimatorParam

Definition Classes
TrainValidRecommendSplitParams
val estimatorParamMaps: ArrayParamMapParam

Definition Classes
TrainValidRecommendSplitParams
val evaluator: EvaluatorParam

Definition Classes
TrainValidRecommendSplitParams
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

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

Definition Classes
Params
def filterRatings(dataset: Dataset[_], itemCol: String, userCol: String): DataFrame

Definition Classes
RankingFunctions
def finalize(): Unit

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

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

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

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

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

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

Definition Classes
AnyRef → Any
def getCollectSubMetrics: Boolean
final def getCollectSubModels: Boolean

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

Definition Classes
Params
def getEstimator: Estimator[_ <: Model[_]]

Definition Classes
TrainValidRecommendSplitParams
def getEstimatorParamMaps: Array[ParamMap]

Definition Classes
TrainValidRecommendSplitParams
def getEvaluator: Evaluator

Definition Classes
TrainValidRecommendSplitParams
def getExecutionContext: ExecutionContext

Definition Classes
RankingTrainValidationSplit → HasParallelism
def getItemCol: String

Definition Classes
HasRecommenderCols
def getMinRatingsI: Int

Definition Classes
TrainValidRecommendSplitParams
def getMinRatingsPerItem: Int

Definition Classes
RankingParams
def getMinRatingsPerUser: Int

Definition Classes
RankingParams
def getMinRatingsU: Int

Definition Classes
TrainValidRecommendSplitParams
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParallelism: Int

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

Definition Classes
Params
def getRatingCol: String

Definition Classes
HasRecommenderCols
final def getSeed: Long

Definition Classes
HasSeed
def getTrainRatio: Double

Definition Classes
TrainValidRecommendSplitParams
def getUserCol: String

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

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

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

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

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

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
val itemCol: Param[String]

Definition Classes
HasRecommenderCols
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 minRatingsI: IntParam

Definition Classes
TrainValidRecommendSplitParams
val minRatingsPerItem: IntParam

Definition Classes
RankingParams
val minRatingsPerUser: IntParam

Definition Classes
RankingParams
val minRatingsU: IntParam

Param for ratio between train and validation data.
Param for ratio between train and validation data. Must be between 0 and 1. Default: 0.75

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val parallelism: IntParam

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

Definition Classes
Params
def prepareTestData(validationDataset: DataFrame, recs: DataFrame, k: Int): Dataset[_]

Definition Classes
RankingFunctions
def prepareTestData(userColumn: String, itemColumn: String, validationDataset: DataFrame, recs: DataFrame, k: Int): Dataset[_]

Definition Classes
RankingFunctions
val ratingCol: Param[String]

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

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final val seed: LongParam

Definition Classes
HasSeed
final def set(paramPair: ParamPair[_]): RankingTrainValidationSplit.this.type

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

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

Definition Classes
Params
def setCollectSubMetrics(value: Boolean): RankingTrainValidationSplit.this.type
def setCollectSubModels(value: Boolean): RankingTrainValidationSplit.this.type

Whether to collect submodels when fitting.
Whether to collect submodels when fitting. If set, we can get submodels from the returned model.
Note: If set this param, when you save the returned model, you can set an option "persistSubModels" to be "true" before saving, in order to save these submodels.
final def setDefault(paramPairs: ParamPair[_]*): RankingTrainValidationSplit.this.type

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

Attributes
protected
Definition Classes
Params
def setEstimator(value: Estimator[_ <: Model[_]]): RankingTrainValidationSplit.this.type
def setEstimatorParamMaps(value: Array[ParamMap]): RankingTrainValidationSplit.this.type
def setEvaluator(value: Evaluator): RankingTrainValidationSplit.this.type
def setItemCol(value: String): RankingTrainValidationSplit.this.type

Definition Classes
HasRecommenderCols
def setMinRatingsPerItem(value: Int): RankingTrainValidationSplit.this.type

Definition Classes
RankingParams
def setMinRatingsPerUser(value: Int): RankingTrainValidationSplit.this.type

Definition Classes
RankingParams
def setParallelism(value: Int): RankingTrainValidationSplit.this.type

Set the mamixum level of parallelism to evaluate models in parallel.
Set the mamixum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation
def setRatingCol(value: String): RankingTrainValidationSplit.this.type

Definition Classes
HasRecommenderCols
def setSeed(value: Long): RankingTrainValidationSplit.this.type
def setTrainRatio(value: Double): RankingTrainValidationSplit.this.type
def setUserCol(value: String): RankingTrainValidationSplit.this.type

Definition Classes
HasRecommenderCols
def split(dataframe: Dataset[_], trainRatio: Double, itemCol: String, userCol: String, ratingCol: String): (DataFrame, DataFrame)

Definition Classes
RankingFunctions
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
val trainRatio: DoubleParam

Definition Classes
TrainValidRecommendSplitParams
def transformSchema(schema: StructType): StructType

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

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
def transformSchemaImpl(schema: StructType): StructType

Attributes
protected
Definition Classes
TrainValidRecommendSplitParams
val uid: String

Definition Classes
RankingTrainValidationSplit → Identifiable
val userCol: Param[String]

Definition Classes
HasRecommenderCols
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( ... )
def write: MLWriter

Definition Classes
ComplexParamsWritable → MLWritable

Inherited from HasCollectSubModels

Inherited from TrainValidRecommendSplitParams

Inherited from HasParallelism

Inherited from HasSeed

Inherited from Wrappable

Inherited from RankingFunctions

Inherited from HasRecommenderCols

Inherited from RankingParams

Inherited from ComplexParamsWritable

Inherited from MLWritable

Inherited from Estimator[RankingTrainValidationSplitModel]

Inherited from PipelineStage

Inherited from org.apache.spark.internal.Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

expertSetParam

Parameters

A list of parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters

Parameter setters

Parameter getters

Members

Related Docs: object RankingTrainValidationSplit | package spark

class RankingTrainValidationSplit extends Estimator[RankingTrainValidationSplitModel] with ComplexParamsWritable with RankingFunctions with TrainValidRecommendSplitParams with HasCollectSubModels

Instance Constructors

new RankingTrainValidationSplit()

new RankingTrainValidationSplit(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

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

def clone(): AnyRef

val collectSubMetrics: BooleanParam

final val collectSubModels: BooleanParam

def copy(extra: ParamMap): RankingTrainValidationSplit

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

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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val estimator: EstimatorParam

val estimatorParamMaps: ArrayParamMapParam

val evaluator: EvaluatorParam

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

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def filterRatings(dataset: Dataset[_], itemCol: String, userCol: String): DataFrame

def finalize(): Unit

def fit(dataset: Dataset[_]): RankingTrainValidationSplitModel

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

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

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

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

final def getClass(): Class[_]

def getCollectSubMetrics: Boolean

final def getCollectSubModels: Boolean

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

def getEstimator: Estimator[_ <: Model[_]]

def getEstimatorParamMaps: Array[ParamMap]

def getEvaluator: Evaluator

def getExecutionContext: ExecutionContext

def getItemCol: String

def getMinRatingsI: Int

def getMinRatingsPerItem: Int

def getMinRatingsPerUser: Int

def getMinRatingsU: Int

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

def getParallelism: Int

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

def getRatingCol: String

final def getSeed: Long

def getTrainRatio: Double

def getUserCol: String

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

val itemCol: Param[String]

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 minRatingsI: IntParam

val minRatingsPerItem: IntParam

val minRatingsPerUser: IntParam