BinaryEstimator

Instance Constructors

new BinaryEstimator(operationName: String, uid: String)(implicit tti1: scala.reflect.api.JavaUniverse.TypeTag[I1], tti2: scala.reflect.api.JavaUniverse.TypeTag[I2], tto: scala.reflect.api.JavaUniverse.TypeTag[O], ttiv1: scala.reflect.api.JavaUniverse.TypeTag[I1.Value], ttiv2: scala.reflect.api.JavaUniverse.TypeTag[I2.Value], ttov: scala.reflect.api.JavaUniverse.TypeTag[O.Value])

operationName
unique name of the operation this stage performs
uid
uid for instance
tti1
type tag for first input
tti2
type tag for second input
tto
type tag for output
ttiv1
type tag for first input value
ttiv2
type tag for second input value
ttov
type tag for output value

Type Members

final type InputFeatures = (FeatureLike[I1], FeatureLike[I2])

Input Features type
Input Features type

Definition Classes
OpPipelineStage2 → OpPipelineStage → InputParams
final type OutputFeatures = FeatureLike[O]

Definition Classes
OpPipelineStage → OpPipelineStageBase

Abstract Value Members

abstract def fitFn(dataset: Dataset[(I1.Value, I2.Value)]): BinaryModel[I1, I2, O]

Function that fits the binary model

Concrete 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 checkInputLength(features: Array[_]): Boolean

Checks the input length
Checks the input length
features
input features
returns
true is input size as expected, false otherwise

Definition Classes
OpPipelineStage2 → InputParams
final def checkSerializable: Try[Unit]

Check if the stage is serializable
Check if the stage is serializable
returns
Failure if not serializable

Definition Classes
BinaryEstimator → OpPipelineStageBase
final def clear(param: Param[_]): BinaryEstimator.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val convertI1: FeatureTypeSparkConverter[I1]
val convertI2: FeatureTypeSparkConverter[I2]
final def copy(extra: ParamMap): BinaryEstimator.this.type

This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).
This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).
Note: that the convention in spark is to have the uid be a constructor argument, so that copies will share a uid with the original (developers should follow this convention).
extra
new parameters want to add to instance
returns
a new instance with the same uid

Definition Classes
OpPipelineStageBase → 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] )
def fit(dataset: Dataset[_]): BinaryModel[I1, I2, O]

Spark operation on dataset to produce RDD for constructor fit function and then turn output function into a Model
Spark operation on dataset to produce RDD for constructor fit function and then turn output function into a Model
dataset
input data for this stage
returns
a fitted model that will perform the transformation specified by the function defined in constructor fit

Definition Classes
BinaryEstimator → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[BinaryModel[I1, I2, O]]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): BinaryModel[I1, I2, O]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BinaryModel[I1, I2, O]

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 getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead
Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead
returns
array of features

Definition Classes
InputParams
Exceptions thrown
NoSuchElementException if the features are not set
RuntimeException in case one of the features is null
final def getInputFeatures(): Array[OPFeature]

Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead
Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead
returns
array of features

Definition Classes
InputParams
Exceptions thrown
NoSuchElementException if the features are not set
RuntimeException in case one of the features is null
final def getInputSchema(): StructType

Definition Classes
OpPipelineStageParams
final def getMetadata(): Metadata

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

Definition Classes
Params
def getOutput(): FeatureLike[O]

Output features that will be created by this stage
Output features that will be created by this stage
returns
feature of type OutputFeatures

Definition Classes
HasOut → OpPipelineStageBase
final def getOutputFeatureName: String

Name of output feature (i.e.
Name of output feature (i.e. column created by this stage)

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

Definition Classes
Params
final def getTransientFeature(i: Int): Option[TransientFeature]

Gets an input feature at index i
Gets an input feature at index i
i
input index
returns
maybe an input feature

Definition Classes
InputParams
final def getTransientFeatures(): Array[TransientFeature]

Gets the input Features
Gets the input Features
returns
input features

Definition Classes
InputParams
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
implicit val i1Encoder: Encoder[I1.Value]
implicit val i2Encoder: Encoder[I2.Value]
final def in1: TransientFeature

Attributes
protected
Definition Classes
HasIn1
final def in2: TransientFeature

Attributes
protected
Definition Classes
HasIn2
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

Attributes
protected
Definition Classes
Logging
final def inputAsArray(in: InputFeatures): Array[OPFeature]

Function to convert InputFeatures to an Array of FeatureLike
Function to convert InputFeatures to an Array of FeatureLike
returns
an Array of FeatureLike

Definition Classes
OpPipelineStage2 → InputParams
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
def onGetMetadata(): Unit

Function to be called on getMetadata
Function to be called on getMetadata

Attributes
protected
Definition Classes
OpPipelineStageParams
def onSetInput(): Unit

Function to be called on setInput
Function to be called on setInput

Attributes
protected
Definition Classes
InputParams
val operationName: String

unique name of the operation this stage performs
unique name of the operation this stage performs

Definition Classes
BinaryEstimator → OpPipelineStageBase
final def outputAsArray(out: OutputFeatures): Array[OPFeature]

Function to convert OutputFeatures to an Array of FeatureLike
Function to convert OutputFeatures to an Array of FeatureLike
returns
an Array of FeatureLike

Definition Classes
OpPipelineStage → OpPipelineStageBase
def outputFeatureUid: String

Attributes
protected[com.salesforce.op]
Definition Classes
OpPipelineStage2 → OpPipelineStage
def outputIsResponse: Boolean

Should output feature be a response? Yes, if any of the input features are.
Should output feature be a response? Yes, if any of the input features are.
returns
true if the the output feature should be a response

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

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

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

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

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

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

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

Attributes
protected
Definition Classes
Params
final def setInput(features: InputFeatures): BinaryEstimator.this.type

Input features that will be used by the stage
Input features that will be used by the stage
returns
feature of type InputFeatures

Definition Classes
OpPipelineStageBase
final def setInputFeatures[S <: OPFeature](features: Array[S]): BinaryEstimator.this.type

Sets input features
Sets input features
S
feature like type
features
array of input features
returns
this stage

Attributes
protected
Definition Classes
InputParams
final def setMetadata(m: Metadata): BinaryEstimator.this.type

Definition Classes
OpPipelineStageParams
def setOutputFeatureName(name: String): BinaryEstimator.this.type

Definition Classes
OpPipelineStage
final def stageName: String

Stage unique name consisting of the stage operation name and uid
Stage unique name consisting of the stage operation name and uid
returns
stage name

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

Definition Classes
AnyRef
def toString(): String

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

This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame
This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame
schema
schema of the input data frame
returns
a new schema with the output features added

Definition Classes
OpPipelineStageBase
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[I1]

type tag for first input
implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[I2]

type tag for second input
implicit val ttiv1: scala.reflect.api.JavaUniverse.TypeTag[I1.Value]

type tag for first input value
implicit val ttiv2: scala.reflect.api.JavaUniverse.TypeTag[I2.Value]

type tag for second input value
implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[O]

type tag for output
type tag for output

Definition Classes
BinaryEstimator → HasOut
implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[O.Value]

type tag for output value
type tag for output value

Definition Classes
BinaryEstimator → HasOut
implicit val tupleEncoder: Encoder[(I1.Value, I2.Value)]
val uid: String

uid for instance
uid for instance

Definition Classes
BinaryEstimator → Identifiable
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( ... )
final def write: MLWriter

Definition Classes
OpPipelineStageBase → MLWritable

Related Doc: package binary

abstract class BinaryEstimator[I1 <: FeatureType, I2 <: FeatureType, O <: FeatureType] extends Estimator[BinaryModel[I1, I2, O]] with OpPipelineStage2[I1, I2, O]

Instance Constructors

Type Members

final type InputFeatures = (FeatureLike[I1], FeatureLike[I2])

final type OutputFeatures = FeatureLike[O]

Abstract Value Members

abstract def fitFn(dataset: Dataset[(I1.Value, I2.Value)]): BinaryModel[I1, I2, O]

Concrete 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 checkInputLength(features: Array[_]): Boolean

final def checkSerializable: Try[Unit]

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

def clone(): AnyRef

val convertI1: FeatureTypeSparkConverter[I1]

val convertI2: FeatureTypeSparkConverter[I2]

final def copy(extra: ParamMap): BinaryEstimator.this.type

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[_]): BinaryModel[I1, I2, O]

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[BinaryModel[I1, I2, O]]

def fit(dataset: Dataset[_], paramMap: ParamMap): BinaryModel[I1, I2, O]

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BinaryModel[I1, I2, O]

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

final def getClass(): Class[_]

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

final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

final def getInputFeatures(): Array[OPFeature]

final def getInputSchema(): StructType

final def getMetadata(): Metadata

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

def getOutput(): FeatureLike[O]

final def getOutputFeatureName: String

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

final def getTransientFeature(i: Int): Option[TransientFeature]

final def getTransientFeatures(): Array[TransientFeature]

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

implicit val i1Encoder: Encoder[I1.Value]

implicit val i2Encoder: Encoder[I2.Value]

final def in1: TransientFeature

final def in2: TransientFeature

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def inputAsArray(in: InputFeatures): Array[OPFeature]

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

def onGetMetadata(): Unit

def onSetInput(): Unit

val operationName: String