DiscretizerModel

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[_]): DiscretizerModel.this.type

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
DiscretizerModel → Model → Transformer → 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
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 finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def get[T](param: Param[T]): Option[T]

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

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

Definition Classes
Params
final def getInputCol: String

Definition Classes
HasInputCol
final def getLabelCol: String

Definition Classes
HasLabelCol
def getMaxBins: Int

Definition Classes
MDLPDiscretizerParams
def getMaxByPart: Int

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

Definition Classes
Params
final def getOutputCol: String

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

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

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

Definition Classes
Params
def hasParent: Boolean

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
final val inputCol: Param[String]

Definition Classes
HasInputCol
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
final val labelCol: Param[String]

Definition Classes
HasLabelCol
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 maxBins: IntParam

Maximum number of buckets into which data points are grouped.
Maximum number of buckets into which data points are grouped. Must be >= 2. default: 2

Definition Classes
MDLPDiscretizerParams
val maxByPart: IntParam

Maximum number of elements to evaluate in each partition.
Maximum number of elements to evaluate in each partition. If this parameter is bigger then the evaluation phase will be sequentially performed. Must be >= 10,000. default: 10,000

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final val outputCol: Param[String]

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

Definition Classes
Params
var parent: Estimator[DiscretizerModel]

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

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

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

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

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

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

Attributes
protected
Definition Classes
Params
def setInputCol(value: String): DiscretizerModel.this.type
def setOutputCol(value: String): DiscretizerModel.this.type
def setParent(parent: Estimator[DiscretizerModel]): DiscretizerModel

Definition Classes
Model
val splits: Array[Array[Float]]

Splits organized by feature.
Splits organized by feature. Each column is the list of splits for a single feature.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: DataFrame): DataFrame

Transform a vector to the discrete space determined by the splits.
Transform a vector to the discrete space determined by the splits. NOTE: Vectors to be transformed must be the same length as the source vectors given to MDLPDiscretizer.fit().

Definition Classes
DiscretizerModel → Transformer
def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

Definition Classes
Transformer
def transform(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@varargs()
def transformSchema(schema: StructType): StructType

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

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

Definition Classes
DiscretizerModel → Identifiable
def validateParams(): Unit

Definition Classes
Params
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
DiscretizerModel → MLWritable
Annotations
@Since( "1.6.0" )

Related Docs: object DiscretizerModel | package feature

class DiscretizerModel extends Model[DiscretizerModel] with MDLPDiscretizerParams with MLWritable

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[_]): DiscretizerModel.this.type

def clone(): AnyRef

def copy(extra: ParamMap): DiscretizerModel

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

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

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

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

final def getClass(): Class[_]

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

final def getInputCol: String

final def getLabelCol: String

def getMaxBins: Int

def getMaxByPart: Int

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

final def getOutputCol: String

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

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

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

final val inputCol: Param[String]

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

final val labelCol: 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 maxBins: IntParam

val maxByPart: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final val outputCol: Param[String]

lazy val params: Array[Param[_]]

var parent: Estimator[DiscretizerModel]

def save(path: String): Unit

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

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

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

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

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

def setInputCol(value: String): DiscretizerModel.this.type

def setOutputCol(value: String): DiscretizerModel.this.type

def setParent(parent: Estimator[DiscretizerModel]): DiscretizerModel

val splits: Array[Array[Float]]

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

def toString(): String

def transform(dataset: DataFrame): DataFrame

def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

def transform(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

def transformSchema(schema: StructType): StructType

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

val uid: String

def validateParams(): Unit