Class

org.apache.spark.mllib.classification

StreamingLogisticRegressionWithSGD

Related Doc: package classification

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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
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Instance Constructors

  1. new StreamingLogisticRegressionWithSGD()

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    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

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  2. final def ##(): Int

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    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

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  4. val algorithm: LogisticRegressionWithSGD

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    The algorithm to use for updating.

    The algorithm to use for updating.

    Attributes
    protected
    Definition Classes
    StreamingLogisticRegressionWithSGD → StreamingLinearAlgorithm

  5. final def asInstanceOf[T0]: T0

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    Any

  6. def clone(): AnyRef

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    protected[java.lang]
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    AnyRef
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    @throws( ... )

  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  13. final def isInstanceOf[T0]: Boolean

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  14. def isTraceEnabled(): Boolean

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    Attributes
    protected
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    Logging

  15. def latestModel(): LogisticRegressionModel

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    Return the latest model.

    Return the latest model.

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since( "1.1.0" )

  16. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging

  17. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging

  18. def logDebug(msg: ⇒ String): Unit

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    Logging

  19. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  20. def logError(msg: ⇒ String): Unit

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    Logging

  21. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  22. def logInfo(msg: ⇒ String): Unit

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    Logging

  23. def logName: String

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    protected
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    Logging

  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  25. def logTrace(msg: ⇒ String): Unit

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    protected
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    Logging

  26. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging

  27. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging

  28. var model: Option[LogisticRegressionModel]

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    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

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    AnyRef

  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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    AnyRef

  32. def predictOn(data: JavaDStream[Vector]): JavaDStream[Double]

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    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]

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    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]

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    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)]

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    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

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    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

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    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

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    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

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    Set the regularization parameter.

    Set the regularization parameter. Default: 0.0.

    Annotations
    @Since( "1.3.0" )

  40. def setStepSize(stepSize: Double): StreamingLogisticRegressionWithSGD.this.type

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    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

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    AnyRef

  42. def toString(): String

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    AnyRef → Any

  43. def trainOn(data: JavaDStream[LabeledPoint]): Unit

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    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

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    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(): Unit

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    AnyRef
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    @throws( ... )

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

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    @throws( ... )

  47. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD]

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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