ViveknSentimentApproach

Instance Constructors

new ViveknSentimentApproach()
new ViveknSentimentApproach(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
def ViveknWordCount(er: ExternalResource, prune: Int, f: (List[String]) ⇒ Set[String], left: Map[String, Long] = ..., right: Map[String, Long] = ...): (Map[String, Long], Map[String, Long])

Definition Classes
ViveknSentimentUtils
final def asInstanceOf[T0]: T0

Definition Classes
Any
def beforeTraining(spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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

Definition Classes
Params
def clone(): AnyRef

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

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
ViveknSentimentApproach → AnnotatorApproach
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
val featureLimit: IntParam

Attributes
protected
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def fit(dataset: Dataset[_]): ViveknSentimentModel

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

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

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

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
def getFeatureLimit(v: Int): Int
def getImportantFeatureRatio(v: Double): Double
def getInputCols: Array[String]

returns
input annotations columns currently used

Definition Classes
AnnotatorApproach → HasInputAnnotationCols
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 getTrainingCols: Array[String]

Definition Classes
AnnotatorApproach
def getUnimportantFeatureStep(v: Double): Double
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 importantFeatureRatio: DoubleParam

Attributes
protected
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[AnnotatorType]

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

Definition Classes
ViveknSentimentApproach → 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
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
def negateSequence(words: Array[String]): Set[String]

Detects negations and transforms them into not_ form
Detects negations and transforms them into not_ form

Definition Classes
ViveknSentimentUtils
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

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

Definition Classes
AnnotatorApproach
val outputAnnotatorType: AnnotatorType

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

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

Definition Classes
Params
val pruneCorpus: IntParam
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
val sentimentCol: Param[String]

Requires sentence boundaries to give score in context Tokenization to make sure tokens are within bounds Transitivity requirements are also required
final def set(paramPair: ParamPair[_]): ViveknSentimentApproach.this.type

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

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

Definition Classes
Params
def setCorpusPrune(value: Int): ViveknSentimentApproach.this.type
final def setDefault(paramPairs: ParamPair[_]*): ViveknSentimentApproach.this.type

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

Attributes
protected
Definition Classes
Params
def setFeatureLimit(v: Int): ViveknSentimentApproach.this.type
def setImportantFeatureRatio(v: Double): ViveknSentimentApproach.this.type
final def setInputCols(value: String*): ViveknSentimentApproach.this.type

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

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

Definition Classes
HasInputAnnotationCols
final def setOutputCol(value: String): ViveknSentimentApproach.this.type

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

Definition Classes
HasOutputAnnotationCol
def setSentimentCol(value: String): ViveknSentimentApproach.this.type
final def setTrainingCols(value: String*): ViveknSentimentApproach.this.type

Definition Classes
AnnotatorApproach
final def setTrainingCols(value: Array[String]): ViveknSentimentApproach.this.type

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

Definition Classes
AnnotatorApproach
def setUnimportantFeatureStep(v: Double): ViveknSentimentApproach.this.type
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]): ViveknSentimentModel

Definition Classes
ViveknSentimentApproach → AnnotatorApproach
lazy val trainingAnnotatorTypes: Array[String]

Definition Classes
AnnotatorApproach
final val trainingCols: StringArrayParam

columns that contain annotations necessary to train this annotator AnnotatorType is used in the same way than input and output annotator types
columns that contain annotations necessary to train this annotator AnnotatorType is used in the same way than input and output annotator types

Attributes
protected
Definition Classes
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
ViveknSentimentApproach → Identifiable
val unimportantFeatureStep: DoubleParam

Attributes
protected
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
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 Doc: package vivekn

class ViveknSentimentApproach extends AnnotatorApproach[ViveknSentimentModel] with ViveknSentimentUtils

Instance Constructors

new ViveknSentimentApproach()

new ViveknSentimentApproach(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 ViveknWordCount(er: ExternalResource, prune: Int, f: (List[String]) ⇒ Set[String], left: Map[String, Long] = ..., right: Map[String, Long] = ...): (Map[String, Long], Map[String, Long])

final def asInstanceOf[T0]: T0

def beforeTraining(spark: SparkSession): Unit

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

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

def clone(): AnyRef

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

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

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

val description: String

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

val featureLimit: IntParam

def finalize(): Unit

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

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

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

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

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

final def getClass(): Class[_]

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

def getFeatureLimit(v: Int): Int

def getImportantFeatureRatio(v: Double): Double

def getInputCols: Array[String]

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

final def getOutputCol: String

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

final def getTrainingCols: Array[String]

def getUnimportantFeatureStep(v: Double): Double

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

val importantFeatureRatio: DoubleParam

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

val inputAnnotatorTypes: Array[AnnotatorType]

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

def negateSequence(words: Array[String]): Set[String]

final def notify(): Unit

final def notifyAll(): Unit

def onTrained(model: ViveknSentimentModel, spark: SparkSession): Unit

val outputAnnotatorType: AnnotatorType

final val outputCol: Param[String]

lazy val params: Array[Param[_]]

val pruneCorpus: IntParam

def save(path: String): Unit