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  • 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.

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    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.

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    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package util
    Definition Classes
    mllib
  • DataValidators
  • KMeansDataGenerator
  • LinearDataGenerator
  • Loader
  • LogisticRegressionDataGenerator
  • MFDataGenerator
  • MLUtils
  • SVMDataGenerator
  • Saveable
o

org.apache.spark.mllib.util

LogisticRegressionDataGenerator

object LogisticRegressionDataGenerator

Generate test data for LogisticRegression. This class chooses positive labels with probability probOne and scales features for positive examples by eps.

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LogisticRegressionDataGenerator.scala
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  9. def generateLogisticRDD(sc: SparkContext, nexamples: Int, nfeatures: Int, eps: Double, nparts: Int = 2, probOne: Double = 0.5): RDD[LabeledPoint]

    Generate an RDD containing test data for LogisticRegression.

    Generate an RDD containing test data for LogisticRegression.

    sc

    SparkContext to use for creating the RDD.

    nexamples

    Number of examples that will be contained in the RDD.

    nfeatures

    Number of features to generate for each example.

    eps

    Epsilon factor by which positive examples are scaled.

    nparts

    Number of partitions of the generated RDD. Default value is 2.

    probOne

    Probability that a label is 1 (and not 0). Default value is 0.5.

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