StochasticHyperopt

Instance Constructors

new StochasticHyperopt(nested: SummarizableEstimator[ModelIn])
new StochasticHyperopt(nested: SummarizableEstimator[ModelIn], uid: String)

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
final def asInstanceOf[T0]: T0

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

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val configurationIndexColumn: Param[String]

Definition Classes
HasConfigurations
val configurations: Block

Definition Classes
HasConfigurations
def copy(extra: ParamMap): StochasticHyperopt[ModelIn]

Definition Classes
StochasticHyperopt → HyperparametersOptimizer → SummarizableEstimator → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
def createConfigDF(config: ParamMap): DataFrame
def createConfigurationsBlock(sqlContext: SQLContext, failedModels: Seq[(ParamMap, Try[ModelIn])], rankedModels: Seq[(ParamMap, ModelIn, Double)]): DataFrame

Create summary block with investigated configurations.
Create summary block with investigated configurations.

Attributes
protected
Definition Classes
HyperparametersOptimizer
def createForkSource(dataset: Dataset[_]): ForkSource[ModelIn, ConfigNumber, ModelIn]

Attributes
protected
Definition Classes
StochasticHyperopt → ForkedEstimator
def createForks(dataset: Dataset[_]): Seq[(ConfigNumber, DataFrame)]

Not used due to custom forks source
Not used due to custom forks source

Attributes
protected
Definition Classes
StochasticHyperopt → ForkedEstimator
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
def diveToReproContext(partialData: (ConfigNumber, DataFrame), estimator: SummarizableEstimator[ModelIn]): Unit

Attributes
protected
Definition Classes
StochasticHyperopt → ForkedEstimator
final val enableDive: BooleanParam

Definition Classes
ForkedEstimator
val epsilonGreedy: DoubleParam
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
val errorColumn: Param[String]

Definition Classes
HasConfigurations
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extract(model: ModelWithSummary[_]): Option[DataFrame]

Definition Classes
MetricsExtractor
def extractBestModel(sqlContext: SQLContext, failedModels: Seq[(ParamMap, Try[ModelIn])], rankedModels: Seq[(ParamMap, ModelIn, Double)]): ModelIn

Given all the history of the optimization create the resulting model with the configurations summary block.
Given all the history of the optimization create the resulting model with the configurations summary block.

Attributes
protected
Definition Classes
HyperparametersOptimizer
def extractConfig(row: Row): (Double, ParamMap)

Extract model configuration from the configurations data frame row.
Extract model configuration from the configurations data frame row.

Attributes
protected
Definition Classes
StochasticHyperopt → HyperparametersOptimizer
def extractConfig(model: ModelIn): (Double, ParamMap)

Definition Classes
HyperparametersOptimizer
val extractExpression: Param[String]

Definition Classes
MetricsExtractor
def extractImpl(model: ModelWithSummary[_]): Option[DataFrame]

Attributes
protected
Definition Classes
HasConfigurations → MetricsExtractor
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
def extractParamsAndQuality(params: ParamMap, model: ModelIn): (ParamMap, ModelIn, Double)

Extracts information of the resulting metrics from the trained model.
Extracts information of the resulting metrics from the trained model.

Attributes
protected
Definition Classes
HyperparametersOptimizer
def failFast(key: ConfigNumber, triedIn: Try[ModelIn]): Try[ModelIn]

Attributes
protected
Definition Classes
StochasticHyperopt → ForkedEstimator
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def finilizeReproContext: ReproContext

Attributes
protected
Definition Classes
ForkedEstimator
def fit(dataset: Dataset[_]): ModelIn

Definition Classes
StochasticHyperopt → ForkedEstimator → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[ModelIn]

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
def fitFork(estimator: SummarizableEstimator[ModelIn], wholeData: Dataset[_], partialData: (ConfigNumber, DataFrame)): (ConfigNumber, Try[ModelIn])

Definition Classes
StochasticHyperopt → ForkedEstimator
final def get[T](param: Param[T]): Option[T]

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

Definition Classes
AnyRef → Any
def getConfigurationIndexColumn: String

Definition Classes
HasConfigurations
def getCurrentContext: Seq[String]

Attributes
protected
Definition Classes
ForkedEstimator
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getErrorColumn: String

Definition Classes
HasConfigurations
def getForkTags(partialData: (ConfigNumber, DataFrame)): Seq[(String, String)]

Attributes
protected
Definition Classes
StochasticHyperopt → ForkedEstimator
final def getMaxIter: Int

Definition Classes
HasMaxIter
def getMetricsBlock: String

Definition Classes
HyperparametersOptimizer
def getMetricsExpression: String

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

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

Definition Classes
Params
def getResultingMetricColumn: String

Definition Classes
HasConfigurations
final def getSeed: Long

Definition Classes
HasSeed
final def getTol: Double

Definition Classes
HasTol
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
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 logMetricsToReproContext(model: ModelIn): Unit

Attributes
protected
Definition Classes
ForkedEstimator
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 val maxIter: IntParam

Definition Classes
HasMaxIter
val maxNoImproveIters: IntParam
def mayBePropagateKey(data: DataFrame, key: Any): DataFrame

Attributes
protected
Definition Classes
ForkedModelParams
def mergeModels(sqlContext: SQLContext, models: Seq[(ConfigNumber, Try[ModelIn])]): ModelIn

Not used due to custom forks source
Not used due to custom forks source

Attributes
protected
Definition Classes
StochasticHyperopt → ForkedEstimator
val metricsBlock: Param[String]

Definition Classes
HyperparametersOptimizer
val metricsExpression: Param[String]

Definition Classes
HyperparametersOptimizer
val nanReplacement: DoubleParam
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val nested: SummarizableEstimator[ModelIn]

Nested estimator to call for each fork.
Nested estimator to call for each fork.

Definition Classes
ForkedEstimator
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final val numThreads: IntParam

Definition Classes
HasNumThreads
final val overwriteModels: BooleanParam

Definition Classes
ForkedEstimator
val paramDomains: Param[Array[ParamDomainPair[_]]]
val paramNames: Param[Map[Param[_], String]]

Definition Classes
HyperparametersOptimizer
lazy val params: Array[Param[_]]

Definition Classes
Params
final val pathForTempModels: Param[String]

Definition Classes
ForkedEstimator
final val persistingKeyColumns: StringArrayParam

Definition Classes
ForkedEstimator
val priorsContextFilter: Param[String]
val priorsPath: Param[String]
val priorsToSample: IntParam
final val propagatedKeyColumn: Param[String]

Definition Classes
ForkedModelParams
def resolveParamName(param: Param[_]): String

Definition Classes
HyperparametersOptimizer
val resultingMetricColumn: Param[String]

Definition Classes
HasConfigurations
val searchMode: Param[BayesianParamOptimizerFactory]
final val seed: LongParam

Definition Classes
HasSeed
final def set(paramPair: ParamPair[_]): StochasticHyperopt.this.type

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

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

Definition Classes
Params
def setConfigurationIndexColumn(value: String): StochasticHyperopt.this.type

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

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

Attributes
protected
Definition Classes
Params
def setEnableDive(value: Boolean): StochasticHyperopt.this.type

Definition Classes
ForkedEstimator
def setEpsilonGreedy(value: Double): StochasticHyperopt.this.type
def setErrorColumn(value: String): StochasticHyperopt.this.type

Definition Classes
HasConfigurations
def setExtractExpression(value: String): StochasticHyperopt.this.type

Definition Classes
MetricsExtractor
def setMaxIter(iters: Int): StochasticHyperopt.this.type
def setMaxNoImproveIters(value: Int): StochasticHyperopt.this.type
def setMetricsBlock(value: String): StochasticHyperopt.this.type

Definition Classes
HyperparametersOptimizer
def setMetricsExpression(value: String): StochasticHyperopt.this.type

Definition Classes
HyperparametersOptimizer
def setNanReplacement(value: Double): StochasticHyperopt.this.type
def setNumThreads(value: Int): StochasticHyperopt.this.type

Definition Classes
HasNumThreads
def setOverwriteModels(value: Boolean): StochasticHyperopt.this.type

Definition Classes
ForkedEstimator
def setParamDomains(pairs: ParamDomainPair[_]*): StochasticHyperopt.this.type
def setParamNames(value: (Param[_], String)*): StochasticHyperopt.this.type

Definition Classes
HyperparametersOptimizer
def setPathForTempModels(value: String): StochasticHyperopt.this.type

Definition Classes
ForkedEstimator
def setPriorsContextFilter(value: String): StochasticHyperopt.this.type
def setPriorsPath(value: String): StochasticHyperopt.this.type
def setPriorsToSample(value: Int): StochasticHyperopt.this.type
def setPropagatedKeyColumn(value: String): StochasticHyperopt.this.type

Definition Classes
ForkedModelParams
def setResultingMetricColumn(value: String): StochasticHyperopt.this.type

Definition Classes
HasConfigurations
def setSearchMode(value: BayesianParamOptimizerFactory): StochasticHyperopt.this.type
def setSeed(value: Long): StochasticHyperopt.this.type
def setTol(value: Double): StochasticHyperopt.this.type
def setTopKForTolerance(value: Int): StochasticHyperopt.this.type
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final val tol: DoubleParam

Definition Classes
HasTol
val topKForTolerance: IntParam
def transformSchema(schema: StructType): StructType

Definition Classes
ForkedEstimator → PipelineStage
Annotations
@DeveloperApi()
def transformSchema(schema: StructType, logging: Boolean): StructType

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

Definition Classes
StochasticHyperopt → ForkedEstimator → Identifiable
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 Doc: package hyperopt

class StochasticHyperopt[ModelIn <: ModelWithSummary[ModelIn]] extends ForkedEstimator[ModelIn, ConfigNumber, ModelIn] with HyperparametersOptimizer[ModelIn] with HasMaxIter with HasTol with HasSeed

Instance Constructors

new StochasticHyperopt(nested: SummarizableEstimator[ModelIn])

new StochasticHyperopt(nested: SummarizableEstimator[ModelIn], uid: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

val configurationIndexColumn: Param[String]

val configurations: Block

def copy(extra: ParamMap): StochasticHyperopt[ModelIn]

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

def createConfigDF(config: ParamMap): DataFrame

def createConfigurationsBlock(sqlContext: SQLContext, failedModels: Seq[(ParamMap, Try[ModelIn])], rankedModels: Seq[(ParamMap, ModelIn, Double)]): DataFrame

def createForkSource(dataset: Dataset[_]): ForkSource[ModelIn, ConfigNumber, ModelIn]

def createForks(dataset: Dataset[_]): Seq[(ConfigNumber, DataFrame)]

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

def diveToReproContext(partialData: (ConfigNumber, DataFrame), estimator: SummarizableEstimator[ModelIn]): Unit

final val enableDive: BooleanParam

val epsilonGreedy: DoubleParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val errorColumn: Param[String]

def explainParam(param: Param[_]): String

def explainParams(): String

final def extract(model: ModelWithSummary[_]): Option[DataFrame]

def extractBestModel(sqlContext: SQLContext, failedModels: Seq[(ParamMap, Try[ModelIn])], rankedModels: Seq[(ParamMap, ModelIn, Double)]): ModelIn

def extractConfig(row: Row): (Double, ParamMap)

def extractConfig(model: ModelIn): (Double, ParamMap)

val extractExpression: Param[String]

def extractImpl(model: ModelWithSummary[_]): Option[DataFrame]

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def extractParamsAndQuality(params: ParamMap, model: ModelIn): (ParamMap, ModelIn, Double)

def failFast(key: ConfigNumber, triedIn: Try[ModelIn]): Try[ModelIn]

def finalize(): Unit

def finilizeReproContext: ReproContext

def fit(dataset: Dataset[_]): ModelIn

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

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

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

def fitFork(estimator: SummarizableEstimator[ModelIn], wholeData: Dataset[_], partialData: (ConfigNumber, DataFrame)): (ConfigNumber, Try[ModelIn])

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

final def getClass(): Class[_]

def getConfigurationIndexColumn: String

def getCurrentContext: Seq[String]

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

def getErrorColumn: String

def getForkTags(partialData: (ConfigNumber, DataFrame)): Seq[(String, String)]

final def getMaxIter: Int

def getMetricsBlock: String

def getMetricsExpression: String

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

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

def getResultingMetricColumn: String

final def getSeed: Long

final def getTol: Double

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

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 logMetricsToReproContext(model: ModelIn): Unit