NerCrfApproach

Instance Constructors

new NerCrfApproach()
new NerCrfApproach(uid: String)

Type Members

type AnnotatorType = String

Definition Classes
HasOutputAnnotatorType

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
def beforeTraining(spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
val c0: IntParam
final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

Attributes
protected
Definition Classes
HasInputAnnotationCols
final def clear(param: Param[_]): NerCrfApproach.this.type

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
AnnotatorApproach → 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
val description: String

Definition Classes
NerCrfApproach → AnnotatorApproach
val entities: StringArrayParam

Definition Classes
NerApproach
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
val externalFeatures: ExternalResourceParam
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 fit(dataset: Dataset[_]): NerCrfModel

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

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

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

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

Definition Classes
Params
def getC0: Int
final def getClass(): Class[_]

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

Definition Classes
Params
def getIncludeConfidence: Boolean
def getInputCols: Array[String]

returns
input annotations columns currently used

Definition Classes
HasInputAnnotationCols
def getL2: Double
def getLossEps: Double
def getMaxEpochs: Int

Definition Classes
NerApproach
def getMinEpochs: Int

Definition Classes
NerApproach
def getMinW: Double
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
final def getOutputCol: String

Gets annotation column name going to generate
Gets annotation column name going to generate

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

Definition Classes
Params
def getRandomSeed: Int

Definition Classes
NerApproach
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
val includeConfidence: BooleanParam
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

Attributes
protected
Definition Classes
Logging
val inputAnnotatorTypes: Array[String]

Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

Definition Classes
NerCrfApproach → HasInputAnnotationCols
final val inputCols: StringArrayParam

columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

Attributes
protected
Definition Classes
HasInputAnnotationCols
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 l2: DoubleParam
val labelColumn: Param[String]

Definition Classes
NerApproach
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 lossEps: DoubleParam
val maxEpochs: IntParam

Definition Classes
NerApproach
val minEpochs: IntParam

Definition Classes
NerApproach
val minW: DoubleParam
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def onTrained(model: NerCrfModel, spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
val outputAnnotatorType: String

Definition Classes
NerCrfApproach → HasOutputAnnotatorType
final val outputCol: Param[String]

Attributes
protected
Definition Classes
HasOutputAnnotationCol
lazy val params: Array[Param[_]]

Definition Classes
Params
val randomSeed: IntParam

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

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

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

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

Definition Classes
Params
def setC0(c0: Int): NerCrfApproach.this.type
final def setDefault(paramPairs: ParamPair[_]*): NerCrfApproach.this.type

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

Attributes
protected
Definition Classes
Params
def setEntities(tags: Array[String]): NerCrfApproach

Definition Classes
NerApproach
def setExternalFeatures(path: String, delimiter: String, readAs: Format = ReadAs.LINE_BY_LINE, options: Map[String, String] = Map("format" -> "text")): NerCrfApproach.this.type
def setExternalFeatures(value: ExternalResource): NerCrfApproach.this.type
def setIncludeConfidence(c: Boolean): NerCrfApproach.this.type
final def setInputCols(value: String*): NerCrfApproach.this.type

Definition Classes
HasInputAnnotationCols
final def setInputCols(value: Array[String]): NerCrfApproach.this.type

Overrides required annotators column if different than default
Overrides required annotators column if different than default

Definition Classes
HasInputAnnotationCols
def setL2(l2: Double): NerCrfApproach.this.type
def setLabelColumn(column: String): NerCrfApproach

Definition Classes
NerApproach
def setLossEps(eps: Double): NerCrfApproach.this.type
def setMaxEpochs(epochs: Int): NerCrfApproach

Definition Classes
NerApproach
def setMinEpochs(epochs: Int): NerCrfApproach

Definition Classes
NerApproach
def setMinW(w: Double): NerCrfApproach.this.type
final def setOutputCol(value: String): NerCrfApproach.this.type

Overrides annotation column name when transforming
Overrides annotation column name when transforming

Definition Classes
HasOutputAnnotationCol
def setRandomSeed(seed: Int): NerCrfApproach

Definition Classes
NerApproach
def setVerbose(verbose: Level): NerCrfApproach

Definition Classes
NerApproach
def setVerbose(verbose: Int): NerCrfApproach

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): NerCrfModel

Definition Classes
NerCrfApproach → AnnotatorApproach
final def transformSchema(schema: StructType): StructType

requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()

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

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

Definition Classes
NerCrfApproach → Identifiable
def validate(schema: StructType): Boolean

takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
schema
to be validated
returns
True if all the required types are present, else false

Attributes
protected
Definition Classes
AnnotatorApproach
val verbose: IntParam

Definition Classes
NerApproach
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
DefaultParamsWritable → MLWritable

Related Docs: object NerCrfApproach | package crf

class NerCrfApproach extends AnnotatorApproach[NerCrfModel] with NerApproach[NerCrfApproach]

Instance Constructors

new NerCrfApproach()

new NerCrfApproach(uid: String)

Type Members

type AnnotatorType = 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

def beforeTraining(spark: SparkSession): Unit

val c0: IntParam

final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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

def clone(): AnyRef

final def copy(extra: ParamMap): Estimator[NerCrfModel]

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

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

val description: String

val entities: StringArrayParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

def explainParams(): String

val externalFeatures: ExternalResourceParam

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

final def fit(dataset: Dataset[_]): NerCrfModel

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

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

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

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

def getC0: Int

final def getClass(): Class[_]

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

def getIncludeConfidence: Boolean

def getInputCols: Array[String]

def getL2: Double

def getLossEps: Double

def getMaxEpochs: Int

def getMinEpochs: Int

def getMinW: Double

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

final def getOutputCol: String

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

def getRandomSeed: Int

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

val includeConfidence: BooleanParam

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

val inputAnnotatorTypes: Array[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

val l2: DoubleParam

val labelColumn: 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 lossEps: DoubleParam

val maxEpochs: IntParam

val minEpochs: IntParam