MetricEvaluator

:: DeveloperApi :: Do no use this directly. Use MetricEvaluator$ instead. This is an implementation of io.prediction.core.BaseEvaluator that evaluates prediction performance based on metric scores.

EI: Evaluation information type
Q: Query class
P: Predicted result class
A: Actual result class
R: Metric result class

Annotations: @DeveloperApi()

Linear Supertypes

BaseEvaluator[EI, Q, P, A, MetricEvaluatorResult[R]], AbstractDoer, Serializable, Serializable, AnyRef, Any

Instance Constructors

new MetricEvaluator(metric: Metric[EI, Q, P, A, R], otherMetrics: Seq[Metric[EI, Q, P, A, _]], outputPath: Option[String])

metric
Primary metric
otherMetrics
Other metrics
outputPath
Optional output path to save evaluation results

Value Members

final def !=(arg0: AnyRef): Boolean

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

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val engineInstances: EngineInstances
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def evaluateBase(sc: SparkContext, evaluation: Evaluation, engineEvalDataSet: Seq[(EngineParams, Seq[(EI, RDD[(Q, P, A)])])], params: WorkflowParams): MetricEvaluatorResult[R]

:: DeveloperApi :: Engine developers should not use this directly.
:: DeveloperApi :: Engine developers should not use this directly. This is called by evaluation workflow to perform evaluation.
sc
Spark context
evaluation
Evaluation to run
engineEvalDataSet
Sets of engine parameters and data for evaluation
params
Evaluation workflow parameters
returns
Evaluation result

Definition Classes
MetricEvaluator → BaseEvaluator
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
lazy val logger: Logger
val metric: Metric[EI, Q, P, A, R]

Primary metric
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val otherMetrics: Seq[Metric[EI, Q, P, A, _]]

Other metrics
val outputPath: Option[String]

Optional output path to save evaluation results
def saveEngineJson(evaluation: Evaluation, engineParams: EngineParams, outputPath: String): Unit
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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( ... )

class MetricEvaluator[EI, Q, P, A, R] extends BaseEvaluator[EI, Q, P, A, MetricEvaluatorResult[R]]

Instance Constructors

new MetricEvaluator(metric: Metric[EI, Q, P, A, R], otherMetrics: Seq[Metric[EI, Q, P, A, _]], outputPath: Option[String])

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val engineInstances: EngineInstances

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def evaluateBase(sc: SparkContext, evaluation: Evaluation, engineEvalDataSet: Seq[(EngineParams, Seq[(EI, RDD[(Q, P, A)])])], params: WorkflowParams): MetricEvaluatorResult[R]

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

lazy val logger: Logger

val metric: Metric[EI, Q, P, A, R]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val otherMetrics: Seq[Metric[EI, Q, P, A, _]]

val outputPath: Option[String]

def saveEngineJson(evaluation: Evaluation, engineParams: EngineParams, outputPath: String): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from BaseEvaluator[EI, Q, P, A, MetricEvaluatorResult[R]]

Inherited from AbstractDoer

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped