PerceptronModel

Instance Constructors

new PerceptronModel()
new PerceptronModel(uid: String)

uid
Internal constructor requirement for serialization of params

Type Members

type AnnotatorType = String

Definition Classes
HasAnnotatorType

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
def annotate(annotations: Seq[Annotation]): Seq[Annotation]

One to one annotation standing from the Tokens perspective, to give each word a corresponding Tag
One to one annotation standing from the Tokens perspective, to give each word a corresponding Tag
annotations
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
returns
any number of annotations processed for every input annotation. Not necessary one to one relationship

Definition Classes
PerceptronModel → AnnotatorModel
val annotatorType: AnnotatorType

Definition Classes
PerceptronModel → HasAnnotatorType
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): PerceptronModel.this.type

Definition Classes
Params
def clone(): AnyRef

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

requirement for annotators copies
requirement for annotators copies

Definition Classes
AnnotatorModel → 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 getInputCols: Array[String]

returns
input annotations columns currently used

Definition Classes
HasInputAnnotationCols
def getModel: AveragedPerceptron
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
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
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
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
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 model: AnnotatorParam[AveragedPerceptron, SerializedPerceptronModel]
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]

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

Definition Classes
Params
var parent: Estimator[PerceptronModel]

Definition Classes
Model
val requiredAnnotatorTypes: Array[AnnotatorType]

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

Definition Classes
PerceptronModel → HasInputAnnotationCols
def save(path: String): Unit

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

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

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

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

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

Attributes
protected
Definition Classes
Params
final def setInputCols(value: String*): PerceptronModel.this.type

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

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

Definition Classes
HasInputAnnotationCols
def setModel(targetModel: AveragedPerceptron): PerceptronModel.this.type
final def setOutputCol(value: String): PerceptronModel.this.type

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

Definition Classes
HasOutputAnnotationCol
def setParent(parent: Estimator[PerceptronModel]): PerceptronModel

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

Definition Classes
AnyRef
def tag(tokenizedSentences: Array[TokenizedSentence]): Array[TaggedSentence]

Tags a group of sentences into POS tagged sentences The logic here is to create a sentence context, run through every word and evaluate its context Based on how frequent a context appears around a word, such context is given a score which is used to predict Some words are marked as non ambiguous from the beginning
Tags a group of sentences into POS tagged sentences The logic here is to create a sentence context, run through every word and evaluate its context Based on how frequent a context appears around a word, such context is given a score which is used to predict Some words are marked as non ambiguous from the beginning
tokenizedSentences
Sentence in the form of single word tokens
returns
A list of sentences which have every word tagged
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final def transform(dataset: Dataset[_]): DataFrame

Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
dataset
Dataset[Row]

Definition Classes
AnnotatorModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
final def transformSchema(schema: StructType): StructType

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

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

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

Internal constructor requirement for serialization of params
Internal constructor requirement for serialization of params

Definition Classes
PerceptronModel → 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
HasInputAnnotationCols
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 PerceptronModel | package perceptron

class PerceptronModel extends AnnotatorModel[PerceptronModel]

Instance Constructors

new PerceptronModel()

new PerceptronModel(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

def annotate(annotations: Seq[Annotation]): Seq[Annotation]

val annotatorType: AnnotatorType

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

def copy(extra: ParamMap): PerceptronModel

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 getInputCols: Array[String]

def getModel: AveragedPerceptron

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

val model: AnnotatorParam[AveragedPerceptron, SerializedPerceptronModel]

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[PerceptronModel]

val requiredAnnotatorTypes: Array[AnnotatorType]

def save(path: String): Unit

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

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

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

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

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

final def setInputCols(value: String*): PerceptronModel.this.type

final def setInputCols(value: Array[String]): PerceptronModel.this.type

def setModel(targetModel: AveragedPerceptron): PerceptronModel.this.type

final def setOutputCol(value: String): PerceptronModel.this.type

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

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

def tag(tokenizedSentences: Array[TokenizedSentence]): Array[TaggedSentence]

def toString(): String