Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package classification
    Definition Classes
    mllib
  • ClassificationModel
  • LogisticRegressionModel
  • LogisticRegressionWithLBFGS
  • LogisticRegressionWithSGD
  • NaiveBayes
  • NaiveBayesModel
  • SVMModel
  • SVMWithSGD
  • StreamingLogisticRegressionWithSGD
c

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, StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD], Logging, AnyRef, Any
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Inherited
  1. StreamingLogisticRegressionWithSGD
  2. Serializable
  3. StreamingLinearAlgorithm
  4. Logging
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. Protected

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")

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  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(entry: LogEntry, throwable: Throwable): Unit
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    protected
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    Logging
  19. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logError(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logError(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logInfo(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logInfo(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  30. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  31. def logTrace(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def logTrace(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  34. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  35. def logWarning(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  36. def logWarning(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. 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
  39. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  40. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  41. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  42. 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")
  43. 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")
  44. 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")
  45. 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")
  46. 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")
  47. 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")
  48. 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")
  49. def setRegParam(regParam: Double): StreamingLogisticRegressionWithSGD.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default: 0.0.

    Annotations
    @Since("1.3.0")
  50. 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")
  51. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  52. def toString(): String
    Definition Classes
    AnyRef → Any
  53. def trainOn(data: JavaDStream[LabeledPoint]): Unit

    Java-friendly version of trainOn.

    Java-friendly version of trainOn.

    Definition Classes
    StreamingLinearAlgorithm
    Annotations
    @Since("1.3.0")
  54. 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")
  55. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  56. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  57. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  58. def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

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

    (Since version 9)

Inherited from Serializable

Inherited from StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD]

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped