Object/Class

org.apache.spark.ml.recommendation

ALS

Related Docs: class ALS | package recommendation

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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()
Source
ALS.scala
Linear Supertypes
Serializable, Serializable, Logging, DefaultParamsReadable[ALS], MLReadable[ALS], AnyRef, Any
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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
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Type Members

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

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

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

    Annotations
    @DeveloperApi()

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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

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    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

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    Any
  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean

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    AnyRef → Any
  8. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]

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    AnyRef → Any
  10. def hashCode(): Int

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    AnyRef → Any
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  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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    Definition Classes
    Any
  14. def isTraceEnabled(): Boolean

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    Attributes
    protected
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    Logging
  15. def load(path: String): ALS

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    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
    ALSMLReadable
    Annotations
    @Since( "1.6.0" )
    Note

    Implementing classes should override this to be Java-friendly.

  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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    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  19. def logError(msg: ⇒ String, throwable: Throwable): Unit

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

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

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

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    protected
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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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    protected
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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
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    Logging
  28. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  29. final def notify(): Unit

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    Definition Classes
    AnyRef
  30. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  31. def read: MLReader[ALS]

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    Returns an MLReader instance for this class.

    Returns an MLReader instance for this class.

    Definition Classes
    DefaultParamsReadableMLReadable
  32. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  33. def toString(): String

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    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])])

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    :: 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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @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