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 recommendation
    Definition Classes
    mllib
  • ALS
  • MatrixFactorizationModel
  • Rating

class MatrixFactorizationModel extends Saveable with Serializable with Logging

Model representing the result of matrix factorization.

Annotations
@Since( "0.8.0" )
Note

If you create the model directly using constructor, please be aware that fast prediction requires cached user/product features and their associated partitioners.

Linear Supertypes
Logging, Serializable, Serializable, Saveable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. MatrixFactorizationModel
  2. Logging
  3. Serializable
  4. Serializable
  5. Saveable
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MatrixFactorizationModel(rank: Int, userFeatures: RDD[(Int, Array[Double])], productFeatures: RDD[(Int, Array[Double])])

    rank

    Rank for the features in this model.

    userFeatures

    RDD of tuples where each tuple represents the userId and the features computed for this user.

    productFeatures

    RDD of tuples where each tuple represents the productId and the features computed for this product.

    Annotations
    @Since( "0.8.0" )

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. val formatVersion: String

    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    MatrixFactorizationModel → Saveable
  10. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  12. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  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 predict(usersProducts: JavaPairRDD[Integer, Integer]): JavaRDD[Rating]

    Java-friendly version of MatrixFactorizationModel.predict.

    Java-friendly version of MatrixFactorizationModel.predict.

    Annotations
    @Since( "1.2.0" )
  32. def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating]

    Predict the rating of many users for many products.

    Predict the rating of many users for many products. The output RDD has an element per each element in the input RDD (including all duplicates) unless a user or product is missing in the training set.

    usersProducts

    RDD of (user, product) pairs.

    returns

    RDD of Ratings.

    Annotations
    @Since( "0.9.0" )
  33. def predict(user: Int, product: Int): Double

    Predict the rating of one user for one product.

    Predict the rating of one user for one product.

    Annotations
    @Since( "0.8.0" )
  34. val productFeatures: RDD[(Int, Array[Double])]
    Annotations
    @Since( "0.8.0" )
  35. val rank: Int
    Annotations
    @Since( "0.8.0" )
  36. def recommendProducts(user: Int, num: Int): Array[Rating]

    Recommends products to a user.

    Recommends products to a user.

    user

    the user to recommend products to

    num

    how many products to return. The number returned may be less than this.

    returns

    Rating objects, each of which contains the given user ID, a product ID, and a "score" in the rating field. Each represents one recommended product, and they are sorted by score, decreasing. The first returned is the one predicted to be most strongly recommended to the user. The score is an opaque value that indicates how strongly recommended the product is.

    Annotations
    @Since( "1.1.0" )
  37. def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])]

    Recommends top products for all users.

    Recommends top products for all users.

    num

    how many products to return for every user.

    returns

    [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of rating objects which contains the same userId, recommended productID and a "score" in the rating field. Semantics of score is same as recommendProducts API

    Annotations
    @Since( "1.4.0" )
  38. def recommendUsers(product: Int, num: Int): Array[Rating]

    Recommends users to a product.

    Recommends users to a product. That is, this returns users who are most likely to be interested in a product.

    product

    the product to recommend users to

    num

    how many users to return. The number returned may be less than this.

    returns

    Rating objects, each of which contains a user ID, the given product ID, and a "score" in the rating field. Each represents one recommended user, and they are sorted by score, decreasing. The first returned is the one predicted to be most strongly recommended to the product. The score is an opaque value that indicates how strongly recommended the user is.

    Annotations
    @Since( "1.1.0" )
  39. def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])]

    Recommends top users for all products.

    Recommends top users for all products.

    num

    how many users to return for every product.

    returns

    [(Int, Array[Rating])] objects, where every tuple contains a productID and an array of rating objects which contains the recommended userId, same productID and a "score" in the rating field. Semantics of score is same as recommendUsers API

    Annotations
    @Since( "1.4.0" )
  40. def save(sc: SparkContext, path: String): Unit

    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes
    MatrixFactorizationModel → Saveable
    Annotations
    @Since( "1.3.0" )
  41. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  42. def toString(): String
    Definition Classes
    AnyRef → Any
  43. val userFeatures: RDD[(Int, Array[Double])]
    Annotations
    @Since( "0.8.0" )
  44. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from Saveable

Inherited from AnyRef

Inherited from Any

Ungrouped