RelationExtractionApproach

Instance Constructors

new RelationExtractionApproach()
new RelationExtractionApproach(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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): GenericClassifierModel

Attributes
protected
Definition Classes
AnnotatorApproach
final def asInstanceOf[T0]: T0

Definition Classes
Any
val batchSize: IntParam

Batch size
Batch size

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

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

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

Definition Classes
Params
def clone(): AnyRef

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

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

Trains TensorFlow model for multi-class text classification
Trains TensorFlow model for multi-class text classification

Definition Classes
RelationExtractionApproach → GenericClassifierApproach → AnnotatorApproach
val dropout: FloatParam

Dropout coefficient
Dropout coefficient

Definition Classes
GenericClassifierApproach
val epochsN: IntParam

Maximum number of epochs to train
Maximum number of epochs to train

Definition Classes
GenericClassifierApproach
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 featureScaling: Param[String]

Feature scaling method.
Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

Definition Classes
GenericClassifierApproach
def finalize(): Unit

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

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

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
val fixImbalance: BooleanParam

Fix the imbalance in the training set by replicating examples of under represented categories
Fix the imbalance in the training set by replicating examples of under represented categories

Definition Classes
GenericClassifierApproach
val fromEntityBeginCol: Param[String]

From entity start position
val fromEntityEndCol: Param[String]

From entity end position
val fromEntityLabelCol: Param[String]

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

Definition Classes
Params
def getBatchSize: Int

Batch size
Batch size

Definition Classes
GenericClassifierApproach
final def getClass(): Class[_]

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

Definition Classes
Params
def getDropout: Float

Dropout coefficient
Dropout coefficient

Definition Classes
GenericClassifierApproach
def getFeatureScaling: String

Get feature scaling method
Get feature scaling method

Definition Classes
GenericClassifierApproach
def getFixImbalance: Boolean

Fix imbalance in training set
Fix imbalance in training set

Definition Classes
GenericClassifierApproach
def getFromEntityBeginCol: String

Get from entity begging
def getFromEntityEndCol: String

Get from entity end
def getFromEntityLabelCol: String

Get from entity label
def getInputCols: Array[String]

Definition Classes
HasInputAnnotationCols
def getLabelColumn: String

Column with label per each document
Column with label per each document

Definition Classes
GenericClassifierApproach
def getLazyAnnotator: Boolean

Definition Classes
CanBeLazy
def getLearningRate: Float

Learning Rate
Learning Rate

Definition Classes
GenericClassifierApproach
def getMaxEpochs: Int

Maximum number of epochs to train
Maximum number of epochs to train

Definition Classes
GenericClassifierApproach
def getModelFile: String

Model file name
Model file name

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

Definition Classes
Params
final def getOutputCol: String

Definition Classes
HasOutputAnnotationCol
def getOutputLogsPath: String

Get output logs path
Get output logs path

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

Definition Classes
Params
def getToEntityBeginCol: String

Get to entity begging
def getToEntityEndCol: String

Get to entity end
def getToEntityLabelCol: String

Get to entity label
def getValidationSplit: Float

Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

Definition Classes
GenericClassifierApproach
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
val inputAnnotatorTypes: Array[AnnotatorType]

Input annotator type : SENTENCE_EMBEDDINGS
Input annotator type : SENTENCE_EMBEDDINGS

Definition Classes
RelationExtractionApproach → GenericClassifierApproach → HasInputAnnotationCols
final val inputCols: StringArrayParam

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 labelColumn: Param[String]

Column with label per each document
Column with label per each document

Definition Classes
GenericClassifierApproach
val lazyAnnotator: BooleanParam

Definition Classes
CanBeLazy
val learningRate: FloatParam

Learning Rate
Learning Rate

Definition Classes
GenericClassifierApproach
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 modelFile: Param[String]

Location of file of the model used for classification
Location of file of the model used for classification

Definition Classes
GenericClassifierApproach
def msgHelper(schema: StructType): String

Attributes
protected
Definition Classes
HasInputAnnotationCols
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: GenericClassifierModel, spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
val outputAnnotatorType: String

Output annotator type : CATEGORY
Output annotator type : CATEGORY

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

Attributes
protected
Definition Classes
HasOutputAnnotationCol
val outputLogsPath: Param[String]

Path to folder to output logs.
Path to folder to output logs. If no path is specified, no logs are generated

Definition Classes
GenericClassifierApproach
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[_]): RelationExtractionApproach.this.type

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

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

Definition Classes
Params
def setBatchSize(batch: Int): RelationExtractionApproach.this.type

Batch size
Batch size

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

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

Attributes
protected
Definition Classes
Params
def setDropout(dropout: Float): RelationExtractionApproach.this.type

Dropout coefficient
Dropout coefficient

Definition Classes
GenericClassifierApproach
def setEpochsNumber(epochs: Int): RelationExtractionApproach.this.type

Maximum number of epochs to train
Maximum number of epochs to train

Definition Classes
GenericClassifierApproach
def setFeatureScaling(featureScaling: String): RelationExtractionApproach.this.type

Set the feature scaling method.
Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

Definition Classes
GenericClassifierApproach
def setFixImbalance(fix: Boolean): RelationExtractionApproach.this.type

Fix imbalance of training set
Fix imbalance of training set

Definition Classes
GenericClassifierApproach
def setFromEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

Set from entity
final def setInputCols(value: String*): RelationExtractionApproach.this.type

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

Definition Classes
HasInputAnnotationCols
def setLabelColumn(column: String): RelationExtractionApproach.this.type

Column with label per each document
Column with label per each document

Definition Classes
GenericClassifierApproach
def setLazyAnnotator(value: Boolean): RelationExtractionApproach.this.type

Definition Classes
CanBeLazy
def setModelFile(modelFile: String): RelationExtractionApproach.this.type

Set the model file name
Set the model file name

Definition Classes
GenericClassifierApproach
final def setOutputCol(value: String): RelationExtractionApproach.this.type

Definition Classes
HasOutputAnnotationCol
def setOutputLogsPath(outputLogsPath: String): RelationExtractionApproach.this.type

Set the output log path
Set the output log path

Definition Classes
GenericClassifierApproach
def setToEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

Set to entity
def setValidationSplit(validationSplit: Float): RelationExtractionApproach.this.type

Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

Definition Classes
GenericClassifierApproach
def setlearningRate(lr: Float): RelationExtractionApproach.this.type

Learning Rate
Learning Rate

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

Definition Classes
AnyRef
val toEntityBeginCol: Param[String]

To entity start position
val toEntityEndCol: Param[String]

To entity end position
val toEntityLabelCol: Param[String]

To entity label
def toString(): String

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

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

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

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

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

Attributes
protected
Definition Classes
AnnotatorApproach
val validationSplit: FloatParam

Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

Definition Classes
GenericClassifierApproach
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 re

class RelationExtractionApproach extends GenericClassifierApproach

Instance Constructors

new RelationExtractionApproach()

new RelationExtractionApproach(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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): GenericClassifierModel

final def asInstanceOf[T0]: T0

val batchSize: IntParam

def beforeTraining(spark: SparkSession): Unit

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

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

def clone(): AnyRef

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

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

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

val description: String

val dropout: FloatParam

val epochsN: IntParam

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 featureScaling: Param[String]

def finalize(): Unit

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

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

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

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

val fixImbalance: BooleanParam

val fromEntityBeginCol: Param[String]

val fromEntityEndCol: Param[String]

val fromEntityLabelCol: Param[String]

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

def getBatchSize: Int

final def getClass(): Class[_]

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

def getDropout: Float

def getFeatureScaling: String

def getFixImbalance: Boolean

def getFromEntityBeginCol: String

def getFromEntityEndCol: String

def getFromEntityLabelCol: String

def getInputCols: Array[String]

def getLabelColumn: String

def getLazyAnnotator: Boolean

def getLearningRate: Float

def getMaxEpochs: Int

def getModelFile: String

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

final def getOutputCol: String

def getOutputLogsPath: String

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

def getToEntityBeginCol: String

def getToEntityEndCol: String

def getToEntityLabelCol: String

def getValidationSplit: Float

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

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

val labelColumn: Param[String]

val lazyAnnotator: BooleanParam

val learningRate: FloatParam