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

object ALS extends Serializable

Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.

Annotations
@Since("0.8.0")
Source
ALS.scala
Linear Supertypes
Serializable, AnyRef, Any
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  1. ALS
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  16. def train(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of ratings by users for a subset of products.

    Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism automatically based on the number of partitions in ratings.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    Annotations
    @Since("0.8.0")
  17. def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of ratings by users for a subset of products.

    Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism automatically based on the number of partitions in ratings.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    Annotations
    @Since("0.8.0")
  18. def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of ratings by users for a subset of products.

    Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    blocks

    level of parallelism to split computation into

    Annotations
    @Since("0.8.0")
  19. def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, seed: Long): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of ratings by users for a subset of products.

    Train a matrix factorization model given an RDD of ratings by users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    blocks

    level of parallelism to split computation into

    seed

    random seed for initial matrix factorization model

    Annotations
    @Since("0.9.1")
  20. def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products.

    Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism determined automatically based on the number of partitions in ratings.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    Annotations
    @Since("0.8.1")
  21. def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products.

    Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism determined automatically based on the number of partitions in ratings.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    alpha

    confidence parameter

    Annotations
    @Since("0.8.1")
  22. def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products.

    Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a configurable level of parallelism.

    ratings

    RDD of Rating objects with userID, productID, and rating

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    blocks

    level of parallelism to split computation into

    alpha

    confidence parameter

    Annotations
    @Since("0.8.1")
  23. def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, blocks: Int, alpha: Double, seed: Long): MatrixFactorizationModel

    Train a matrix factorization model given an RDD of 'implicit preferences' given by users to some products, in the form of (userID, productID, preference) pairs.

    Train a matrix factorization model given an RDD of 'implicit preferences' given by users to some products, in the form of (userID, productID, preference) pairs. We approximate the ratings matrix as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, we run a given number of iterations of ALS. This is done using a level of parallelism given by blocks.

    ratings

    RDD of (userID, productID, rating) pairs

    rank

    number of features to use (also referred to as the number of latent factors)

    iterations

    number of iterations of ALS

    lambda

    regularization parameter

    blocks

    level of parallelism to split computation into

    alpha

    confidence parameter

    seed

    random seed for initial matrix factorization model

    Annotations
    @Since("0.8.1")
  24. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
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