QuantileDiscretizer

QuantileDiscretizer takes a column with continuous features and outputs a column with binned categorical features. The number of bins can be set using the numBuckets parameter. It is possible that the number of buckets used will be smaller than this value, for example, if there are too few distinct values of the input to create enough distinct quantiles. Since 2.3.0, QuantileDiscretizer can map multiple columns at once by setting the inputCols parameter. If both of the inputCol and inputCols parameters are set, an Exception will be thrown. To specify the number of buckets for each column, the numBucketsArray parameter can be set, or if the number of buckets should be the same across columns, numBuckets can be set as a convenience.

NaN handling: null and NaN values will be ignored from the column during QuantileDiscretizer fitting. This will produce a Bucketizer model for making predictions. During the transformation, Bucketizer will raise an error when it finds NaN values in the dataset, but the user can also choose to either keep or remove NaN values within the dataset by setting handleInvalid. If the user chooses to keep NaN values, they will be handled specially and placed into their own bucket, for example, if 4 buckets are used, then non-NaN data will be put into buckets[0-3], but NaNs will be counted in a special bucket[4].

Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for a detailed description). The precision of the approximation can be controlled with the relativeError parameter. The lower and upper bin bounds will be -Infinity and +Infinity, covering all real values.

Annotations: @Since( "1.6.0" )

Linear Supertypes

HasOutputCols, HasInputCols, DefaultParamsWritable, MLWritable, QuantileDiscretizerBase, HasOutputCol, HasInputCol, HasHandleInvalid, Estimator[Bucketizer], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

Instance Constructors

new QuantileDiscretizer()

Annotations
@Since( "1.6.0" )
new QuantileDiscretizer(uid: String)

Annotations
@Since( "1.6.0" )

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[_]): QuantileDiscretizer.this.type

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
QuantileDiscretizer → Estimator → PipelineStage → Params
Annotations
@Since( "1.6.0" )
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] )
def fit(dataset: Dataset[_]): Bucketizer

Definition Classes
QuantileDiscretizer → Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[Bucketizer]

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

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

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

Definition Classes
Params
final def getHandleInvalid: String

Definition Classes
HasHandleInvalid
def getInOutCols: (Array[String], Array[String])
final def getInputCol: String

Definition Classes
HasInputCol
final def getInputCols: Array[String]

Definition Classes
HasInputCols
def getNumBuckets: Int

Definition Classes
QuantileDiscretizerBase
def getNumBucketsArray: Array[Int]

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

Definition Classes
Params
final def getOutputCol: String

Definition Classes
HasOutputCol
final def getOutputCols: Array[String]

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

Definition Classes
Params
def getRelativeError: Double

Definition Classes
QuantileDiscretizerBase
val handleInvalid: Param[String]

Param for how to handle invalid entries.
Param for how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Note that in the multiple columns case, the invalid handling is applied to all columns. That said for 'error' it will throw an error if any invalids are found in any column, for 'skip' it will skip rows with any invalids in any columns, etc. Default: "error"

Definition Classes
QuantileDiscretizerBase → HasHandleInvalid
Annotations
@Since( "2.1.0" )
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 val inputCol: Param[String]

Definition Classes
HasInputCol
final val inputCols: StringArrayParam

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numBuckets: IntParam

Number of buckets (quantiles, or categories) into which data points are grouped.
Number of buckets (quantiles, or categories) into which data points are grouped. Must be greater than or equal to 2.
See also handleInvalid, which can optionally create an additional bucket for NaN values.
default: 2

Definition Classes
QuantileDiscretizerBase
val numBucketsArray: IntArrayParam

Array of number of buckets (quantiles, or categories) into which data points are grouped.
Array of number of buckets (quantiles, or categories) into which data points are grouped. Each value must be greater than or equal to 2
See also handleInvalid, which can optionally create an additional bucket for NaN values.

Definition Classes
QuantileDiscretizerBase
final val outputCol: Param[String]

Definition Classes
HasOutputCol
final val outputCols: StringArrayParam

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

Definition Classes
Params
val relativeError: DoubleParam

Relative error (see documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for description) Must be in the range [0, 1].
Relative error (see documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for description) Must be in the range [0, 1]. Note that in multiple columns case, relative error is applied to all columns. default: 0.001

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

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

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

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

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

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

Attributes
protected
Definition Classes
Params
def setHandleInvalid(value: String): QuantileDiscretizer.this.type

Annotations
@Since( "2.1.0" )
def setInputCol(value: String): QuantileDiscretizer.this.type

Annotations
@Since( "1.6.0" )
def setInputCols(value: Array[String]): QuantileDiscretizer.this.type

Annotations
@Since( "2.3.0" )
def setNumBuckets(value: Int): QuantileDiscretizer.this.type

Annotations
@Since( "1.6.0" )
def setNumBucketsArray(value: Array[Int]): QuantileDiscretizer.this.type

Annotations
@Since( "2.3.0" )
def setOutputCol(value: String): QuantileDiscretizer.this.type

Annotations
@Since( "1.6.0" )
def setOutputCols(value: Array[String]): QuantileDiscretizer.this.type

Annotations
@Since( "2.3.0" )
def setRelativeError(value: Double): QuantileDiscretizer.this.type

Annotations
@Since( "2.0.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transformSchema(schema: StructType): StructType

Definition Classes
QuantileDiscretizer → PipelineStage
Annotations
@Since( "1.6.0" )
def transformSchema(schema: StructType, logging: Boolean): StructType

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

Definition Classes
QuantileDiscretizer → Identifiable
Annotations
@Since( "1.6.0" )
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
QuantileDiscretizer → DefaultParamsWritable → MLWritable

Related Docs: object QuantileDiscretizer | package odkl

final class QuantileDiscretizer extends Estimator[Bucketizer] with QuantileDiscretizerBase with DefaultParamsWritable with HasInputCols with HasOutputCols

Instance Constructors

new QuantileDiscretizer()

new QuantileDiscretizer(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[_]): QuantileDiscretizer.this.type

def clone(): AnyRef

def copy(extra: ParamMap): QuantileDiscretizer

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

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

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

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

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

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

final def getClass(): Class[_]

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

final def getHandleInvalid: String

def getInOutCols: (Array[String], Array[String])

final def getInputCol: String

final def getInputCols: Array[String]

def getNumBuckets: Int

def getNumBucketsArray: Array[Int]

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

final def getOutputCol: String

final def getOutputCols: Array[String]

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

def getRelativeError: Double

val handleInvalid: Param[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 val inputCol: Param[String]

final val inputCols: StringArrayParam

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numBuckets: IntParam

val numBucketsArray: IntArrayParam

final val outputCol: Param[String]

final val outputCols: StringArrayParam

lazy val params: Array[Param[_]]

val relativeError: DoubleParam

def save(path: String): Unit

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

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

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

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