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 ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package recommendation
    Definition Classes
    ml
  • ALS
  • ALSModel

object ALS extends DefaultParamsReadable[ALS] with Logging with Serializable

:: DeveloperApi :: An implementation of ALS that supports generic ID types, specialized for Int and Long. This is exposed as a developer API for users who do need other ID types. But it is not recommended because it increases the shuffle size and memory requirement during training. For simplicity, users and items must have the same type. The number of distinct users/items should be smaller than 2 billion.

Annotations
@DeveloperApi()
Linear Supertypes
Serializable, Serializable, Logging, DefaultParamsReadable[ALS], MLReadable[ALS], AnyRef, Any
Ordering
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Inherited
  1. ALS
  2. Serializable
  3. Serializable
  4. Logging
  5. DefaultParamsReadable
  6. MLReadable
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Type Members

  1. case class Rating[ID](user: ID, item: ID, rating: Float) extends Product with Serializable

    :: DeveloperApi :: Rating class for better code readability.

    :: DeveloperApi :: Rating class for better code readability.

    Annotations
    @DeveloperApi()

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): 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 load(path: String): ALS

    Reads an ML instance from the input path, a shortcut of read.load(path).

    Reads an ML instance from the input path, a shortcut of read.load(path).

    Definition Classes
    ALS → MLReadable
    Annotations
    @Since( "1.6.0" )
    Note

    Implementing classes should override this to be Java-friendly.

  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. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  29. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  30. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  31. def read: MLReader[ALS]

    Returns an MLReader instance for this class.

    Returns an MLReader instance for this class.

    Definition Classes
    DefaultParamsReadable → MLReadable
  32. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  33. def toString(): String
    Definition Classes
    AnyRef → Any
  34. def train[ID](ratings: RDD[Rating[ID]], rank: Int = 10, numUserBlocks: Int = 10, numItemBlocks: Int = 10, maxIter: Int = 10, regParam: Double = 0.1, implicitPrefs: Boolean = false, alpha: Double = 1.0, nonnegative: Boolean = false, intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK, checkpointInterval: Int = 10, seed: Long = 0L)(implicit arg0: ClassTag[ID], ord: Ordering[ID]): (RDD[(ID, Array[Float])], RDD[(ID, Array[Float])])

    :: DeveloperApi :: Implementation of the ALS algorithm.

    :: DeveloperApi :: Implementation of the ALS algorithm.

    This implementation of the ALS factorization algorithm partitions the two sets of factors among Spark workers so as to reduce network communication by only sending one copy of each factor vector to each Spark worker on each iteration, and only if needed. This is achieved by precomputing some information about the ratings matrix to determine which users require which item factors and vice versa. See the Scaladoc for InBlock for a detailed explanation of how the precomputation is done.

    In addition, since each iteration of calculating the factor matrices depends on the known ratings, which are spread across Spark partitions, a naive implementation would incur significant network communication overhead between Spark workers, as the ratings RDD would be repeatedly shuffled during each iteration. This implementation reduces that overhead by performing the shuffling operation up front, precomputing each partition's ratings dependencies and duplicating those values to the appropriate workers before starting iterations to solve for the factor matrices. See the Scaladoc for OutBlock for a detailed explanation of how the precomputation is done.

    Note that the term "rating block" is a bit of a misnomer, as the ratings are not partitioned by contiguous blocks from the ratings matrix but by a hash function on the rating's location in the matrix. If it helps you to visualize the partitions, it is easier to think of the term "block" as referring to a subset of an RDD containing the ratings rather than a contiguous submatrix of the ratings matrix.

    Annotations
    @DeveloperApi()
  35. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from DefaultParamsReadable[ALS]

Inherited from MLReadable[ALS]

Inherited from AnyRef

Inherited from Any

Members