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 optimization
    Definition Classes
    mllib
  • Gradient
  • GradientDescent
  • HingeGradient
  • L1Updater
  • LBFGS
  • LeastSquaresGradient
  • LogisticGradient
  • Optimizer
  • SimpleUpdater
  • SquaredL2Updater
  • Updater

object LBFGS extends Logging with Serializable

Top-level method to run L-BFGS.

Source
LBFGS.scala
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Serializable, Serializable, Logging, AnyRef, Any
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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. def isTraceEnabled(): Boolean
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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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  29. final def notifyAll(): Unit
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  30. def runLBFGS(data: RDD[(Double, Vector)], gradient: Gradient, updater: Updater, numCorrections: Int, convergenceTol: Double, maxNumIterations: Int, regParam: Double, initialWeights: Vector): (Vector, Array[Double])

    Run Limited-memory BFGS (L-BFGS) in parallel.

    Run Limited-memory BFGS (L-BFGS) in parallel. Averaging the subgradients over different partitions is performed using one standard spark map-reduce in each iteration.

    data

    - Input data for L-BFGS. RDD of the set of data examples, each of the form (label, [feature values]).

    gradient

    - Gradient object (used to compute the gradient of the loss function of one single data example)

    updater

    - Updater function to actually perform a gradient step in a given direction.

    numCorrections

    - The number of corrections used in the L-BFGS update.

    convergenceTol

    - The convergence tolerance of iterations for L-BFGS which is must be nonnegative. Lower values are less tolerant and therefore generally cause more iterations to be run.

    maxNumIterations

    - Maximal number of iterations that L-BFGS can be run.

    regParam

    - Regularization parameter

    returns

    A tuple containing two elements. The first element is a column matrix containing weights for every feature, and the second element is an array containing the loss computed for every iteration.

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