Engine

Instance Constructors

new Engine(dataSourceClass: Class[_ <: BaseDataSource[TD, EI, Q, A]], preparatorClass: Class[_ <: BasePreparator[TD, PD]], algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]], servingClass: Class[_ <: BaseServing[Q, P]])

Java-friendly constructor
Java-friendly constructor
dataSourceClass
Data source class.
preparatorClass
Preparator class.
algorithmClassMap
Map of algorithm names to classes.
servingClass
Serving class.
new Engine(dataSourceClass: Class[_ <: BaseDataSource[TD, EI, Q, A]], preparatorClass: Class[_ <: BasePreparator[TD, PD]], algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]], servingClass: Class[_ <: BaseServing[Q, P]])

This auxiliary constructor is provided for backward compatibility.
This auxiliary constructor is provided for backward compatibility.
dataSourceClass
Data source class.
preparatorClass
Preparator class.
algorithmClassMap
Map of algorithm names to classes.
servingClass
Serving class.
new Engine(dataSourceClassMap: Map[String, Class[_ <: BaseDataSource[TD, EI, Q, A]]], preparatorClassMap: Map[String, Class[_ <: BasePreparator[TD, PD]]], algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]], servingClassMap: Map[String, Class[_ <: BaseServing[Q, P]]])

dataSourceClassMap
Map of data source names to class.
preparatorClassMap
Map of preparator names to class.
algorithmClassMap
Map of algorithm names to classes.
servingClassMap
Map of serving names to class.

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]]

Map of algorithm names to classes.
final def asInstanceOf[T0]: T0

Definition Classes
Any
def batchEval(sc: SparkContext, engineParamsList: Seq[EngineParams], params: WorkflowParams): Seq[(EngineParams, Seq[(EI, RDD[(Q, P, A)])])]

:: DeveloperApi :: Override this method to further optimize the process that runs multiple evaluations (during tuning, for example).
:: DeveloperApi :: Override this method to further optimize the process that runs multiple evaluations (during tuning, for example). By default, this method calls eval for each element in the engine parameters list.
sc
An instance of SparkContext.
engineParamsList
A list of EngineParams for running batch evaluation.
params
An instance of WorkflowParams that controls the workflow.
returns
A list of engine parameters and evaluation result (from eval) tuples.

Definition Classes
BaseEngine
Annotations
@DeveloperApi()
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(dataSourceClassMap: Map[String, Class[_ <: BaseDataSource[TD, EI, Q, A]]] = dataSourceClassMap, preparatorClassMap: Map[String, Class[_ <: BasePreparator[TD, PD]]] = preparatorClassMap, algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]] = algorithmClassMap, servingClassMap: Map[String, Class[_ <: BaseServing[Q, P]]] = servingClassMap): Engine[TD, EI, PD, Q, P, A]

Returns a new Engine instance, mimicking case class's copy method behavior.
val dataSourceClassMap: Map[String, Class[_ <: BaseDataSource[TD, EI, Q, A]]]

Map of data source names to class.
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def eval(sc: SparkContext, engineParams: EngineParams, params: WorkflowParams): Seq[(EI, RDD[(Q, P, A)])]

This is implemented such that org.apache.predictionio.controller.Evaluation can use this method to generate inputs for org.apache.predictionio.controller.Metric.
This is implemented such that org.apache.predictionio.controller.Evaluation can use this method to generate inputs for org.apache.predictionio.controller.Metric.
sc
An instance of SparkContext.
engineParams
An instance of EngineParams for running a single evaluation.
params
An instance of WorkflowParams that controls the workflow.
returns
A list of evaluation information and RDD of query, predicted result, and actual result tuple tuple.

Definition Classes
Engine → BaseEngine
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def jValueToEngineParams(variantJson: JValue, jsonExtractor: JsonExtractorOption): EngineParams

:: DeveloperApi :: Implement this method to convert a JValue (read from an engine variant JSON file) to an instance of EngineParams.
:: DeveloperApi :: Implement this method to convert a JValue (read from an engine variant JSON file) to an instance of EngineParams.
variantJson
Content of the engine variant JSON as JValue.
jsonExtractor
Content of the engine variant JSON as JValue.
returns
An instance of EngineParams converted from JSON.

Definition Classes
Engine → BaseEngine
lazy val logger: Logger

Attributes
protected
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val preparatorClassMap: Map[String, Class[_ <: BasePreparator[TD, PD]]]

Map of preparator names to class.
val servingClassMap: Map[String, Class[_ <: BaseServing[Q, P]]]

Map of serving names to class.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(sc: SparkContext, engineParams: EngineParams, engineInstanceId: String, params: WorkflowParams): Seq[Any]

Training this engine would return a list of models.
Training this engine would return a list of models.
sc
An instance of SparkContext.
engineParams
An instance of EngineParams for running a single training.
params
An instance of WorkflowParams that controls the workflow.
returns
A list of models.

Definition Classes
Engine → BaseEngine
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( ... )

Related Docs: object Engine | package controller

class Engine[TD, EI, PD, Q, P, A] extends BaseEngine[EI, Q, P, A]

Instance Constructors

new Engine(dataSourceClass: Class[_ <: BaseDataSource[TD, EI, Q, A]], preparatorClass: Class[_ <: BasePreparator[TD, PD]], algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]], servingClass: Class[_ <: BaseServing[Q, P]])

new Engine(dataSourceClass: Class[_ <: BaseDataSource[TD, EI, Q, A]], preparatorClass: Class[_ <: BasePreparator[TD, PD]], algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]], servingClass: Class[_ <: BaseServing[Q, P]])

new Engine(dataSourceClassMap: Map[String, Class[_ <: BaseDataSource[TD, EI, Q, A]]], preparatorClassMap: Map[String, Class[_ <: BasePreparator[TD, PD]]], algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]], servingClassMap: Map[String, Class[_ <: BaseServing[Q, P]]])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val algorithmClassMap: Map[String, Class[_ <: BaseAlgorithm[PD, _, Q, P]]]

final def asInstanceOf[T0]: T0

def batchEval(sc: SparkContext, engineParamsList: Seq[EngineParams], params: WorkflowParams): Seq[(EngineParams, Seq[(EI, RDD[(Q, P, A)])])]

def clone(): AnyRef

val dataSourceClassMap: Map[String, Class[_ <: BaseDataSource[TD, EI, Q, A]]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def eval(sc: SparkContext, engineParams: EngineParams, params: WorkflowParams): Seq[(EI, RDD[(Q, P, A)])]

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def jValueToEngineParams(variantJson: JValue, jsonExtractor: JsonExtractorOption): EngineParams

lazy val logger: Logger

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val preparatorClassMap: Map[String, Class[_ <: BasePreparator[TD, PD]]]

val servingClassMap: Map[String, Class[_ <: BaseServing[Q, P]]]

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

def toString(): String

def train(sc: SparkContext, engineParams: EngineParams, engineInstanceId: String, params: WorkflowParams): Seq[Any]

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from BaseEngine[EI, Q, P, A]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped