StreamingLinearAlgorithm

:: DeveloperApi :: StreamingLinearAlgorithm implements methods for continuously training a generalized linear model model on streaming data, and using it for prediction on (possibly different) streaming data.

This class takes as type parameters a GeneralizedLinearModel, and a GeneralizedLinearAlgorithm, making it easy to extend to construct streaming versions of any analyses using GLMs. Initial weights must be set before calling trainOn or predictOn. Only weights will be updated, not an intercept. If the model needs an intercept, it should be manually appended to the input data.

For example usage, see StreamingLinearRegressionWithSGD.

NOTE(Freeman): In some use cases, the order in which trainOn and predictOn are called in an application will affect the results. When called on the same DStream, if trainOn is called before predictOn, when new data arrive the model will update and the prediction will be based on the new model. Whereas if predictOn is called first, the prediction will use the model from the previous update.

NOTE(Freeman): It is ok to call predictOn repeatedly on multiple streams; this will generate predictions for each one all using the current model. It is also ok to call trainOn on different streams; this will update the model using each of the different sources, in sequence.

Annotations: @DeveloperApi()

Linear Supertypes

Logging, AnyRef, Any

Known Subclasses

StreamingLinearRegressionWithSGD

Instance Constructors

new StreamingLinearAlgorithm()

Abstract Value Members

abstract val algorithm: A

The algorithm to use for updating.
The algorithm to use for updating.

Attributes
protected
abstract val model: M

The model to be updated and used for prediction.
The model to be updated and used for prediction.

Attributes
protected

Concrete Value Members

final def !=(arg0: AnyRef): Boolean

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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Any
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def latestModel(): M

Return the latest model.
def log: Logger

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protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

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protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

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protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

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protected
Definition Classes
Logging
def logName: String

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protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

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protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

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

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final def notify(): Unit

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final def notifyAll(): Unit

Definition Classes
AnyRef
def predictOn(data: DStream[Vector]): DStream[Double]

Use the model to make predictions on batches of data from a DStream
Use the model to make predictions on batches of data from a DStream
data
DStream containing feature vectors
returns
DStream containing predictions
def predictOnValues[K](data: DStream[(K, Vector)])(implicit arg0: ClassTag[K]): DStream[(K, Double)]

Use the model to make predictions on the values of a DStream and carry over its keys.
Use the model to make predictions on the values of a DStream and carry over its keys.
K
key type
data
DStream containing feature vectors
returns
DStream containing the input keys and the predictions as values
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def trainOn(data: DStream[LabeledPoint]): Unit

Update the model by training on batches of data from a DStream.
Update the model by training on batches of data from a DStream. This operation registers a DStream for training the model, and updates the model based on every subsequent batch of data from the stream.
data
DStream containing labeled data
final def wait(): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

abstract class StreamingLinearAlgorithm[M <: GeneralizedLinearModel, A <: GeneralizedLinearAlgorithm[M]] extends Logging

Instance Constructors

new StreamingLinearAlgorithm()

Abstract Value Members

abstract val algorithm: A

abstract val model: M

Concrete 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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def latestModel(): M

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

final def notify(): Unit

final def notifyAll(): Unit

def predictOn(data: DStream[Vector]): DStream[Double]

def predictOnValues[K](data: DStream[(K, Vector)])(implicit arg0: ClassTag[K]): DStream[(K, Double)]

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

def toString(): String

def trainOn(data: DStream[LabeledPoint]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped