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org.apache.spark.mllib.classification

StreamingLogisticRegressionWithSGD

class StreamingLogisticRegressionWithSGD extends StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD] with Serializable

Train or predict a logistic regression model on streaming data. Training uses Stochastic Gradient Descent to update the model based on each new batch of incoming data from a DStream (see LogisticRegressionWithSGD for model equation)

Each batch of data is assumed to be an RDD of LabeledPoints. The number of data points per batch can vary, but the number of features must be constant. An initial weight vector must be provided.

Use a builder pattern to construct a streaming logistic regression analysis in an application, like:

val model = new StreamingLogisticRegressionWithSGD()
  .setStepSize(0.5)
  .setNumIterations(10)
  .setInitialWeights(Vectors.dense(...))
  .trainOn(DStream)
Annotations
@Since( "1.3.0" )
Source
StreamingLogisticRegressionWithSGD.scala
Linear Supertypes
Serializable, Serializable, StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD], Logging, AnyRef, Any
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  1. StreamingLogisticRegressionWithSGD
  2. Serializable
  3. Serializable
  4. StreamingLinearAlgorithm
  5. Logging
  6. AnyRef
  7. Any
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Instance Constructors

  1. new StreamingLogisticRegressionWithSGD()

    Construct a StreamingLogisticRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.

    Construct a StreamingLogisticRegression object with default parameters: {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}. Initial weights must be set before using trainOn or predictOn (see StreamingLinearAlgorithm)

    Annotations
    @Since( "1.3.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val algorithm: LogisticRegressionWithSGD

    The algorithm to use for updating.

    The algorithm to use for updating.

    Attributes
    protected
    Definition Classes
    StreamingLogisticRegressionWithSGD → StreamingLinearAlgorithm
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @IntrinsicCandidate()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  15. def latestModel(): LogisticRegressionModel

    Return the latest model.

    Return the latest model.

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.1.0" )
  16. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  17. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  19. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. var model: Option[LogisticRegressionModel]

    The model to be updated and used for prediction.

    The model to be updated and used for prediction.

    Attributes
    protected
    Definition Classes
    StreamingLogisticRegressionWithSGD → StreamingLinearAlgorithm
  29. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  31. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  32. def predictOn(data: JavaDStream[Vector]): JavaDStream[Double]

    Java-friendly version of predictOn.

    Java-friendly version of predictOn.

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.3.0" )
  33. 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

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.1.0" )
  34. def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, Double]

    Java-friendly version of predictOnValues.

    Java-friendly version of predictOnValues.

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.3.0" )
  35. 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

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.1.0" )
  36. def setInitialWeights(initialWeights: Vector): StreamingLogisticRegressionWithSGD.this.type

    Set the initial weights.

    Set the initial weights. Default: [0.0, 0.0].

    Annotations
    @Since( "1.3.0" )
  37. def setMiniBatchFraction(miniBatchFraction: Double): StreamingLogisticRegressionWithSGD.this.type

    Set the fraction of each batch to use for updates.

    Set the fraction of each batch to use for updates. Default: 1.0.

    Annotations
    @Since( "1.3.0" )
  38. def setNumIterations(numIterations: Int): StreamingLogisticRegressionWithSGD.this.type

    Set the number of iterations of gradient descent to run per update.

    Set the number of iterations of gradient descent to run per update. Default: 50.

    Annotations
    @Since( "1.3.0" )
  39. def setRegParam(regParam: Double): StreamingLogisticRegressionWithSGD.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default: 0.0.

    Annotations
    @Since( "1.3.0" )
  40. def setStepSize(stepSize: Double): StreamingLogisticRegressionWithSGD.this.type

    Set the step size for gradient descent.

    Set the step size for gradient descent. Default: 0.1.

    Annotations
    @Since( "1.3.0" )
  41. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  42. def toString(): String
    Definition Classes
    AnyRef → Any
  43. def trainOn(data: JavaDStream[LabeledPoint]): Unit

    Java-friendly version of trainOn.

    Java-friendly version of trainOn.

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.3.0" )
  44. 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

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.1.0" )
  45. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
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    @throws( ... )
  46. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  47. final def wait(): Unit
    Definition Classes
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    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
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    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Serializable

Inherited from Serializable

Inherited from StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD]

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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