org.apache.spark.mllib.recommendation
Class ALS

Object
  org.apache.spark.mllib.recommendation.ALS

All Implemented Interfaces:

java.io.Serializable, Logging

public class ALS
extends Object
implements scala.Serializable, Logging

See Also:

Serialized Form

Constructor Summary

ALS()

Method Summary

MatrixFactorizationModel	run(JavaRDD<Rating> ratings)
MatrixFactorizationModel	run(RDD<Rating> ratings)
ALS	setAlpha(double alpha)
ALS	setBlocks(int numBlocks)
ALS	setCheckpointInterval(int checkpointInterval)
ALS	setFinalRDDStorageLevel(StorageLevel storageLevel)
ALS	setImplicitPrefs(boolean implicitPrefs)
ALS	setIntermediateRDDStorageLevel(StorageLevel storageLevel)
ALS	setIterations(int iterations)
ALS	setLambda(double lambda)
ALS	setNonnegative(boolean b)
ALS	setProductBlocks(int numProductBlocks)
ALS	setRank(int rank)
ALS	setSeed(long seed)
ALS	setUserBlocks(int numUserBlocks)
static MatrixFactorizationModel	train(RDD<Rating> ratings, int rank, int iterations) Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) pairs.
static MatrixFactorizationModel	train(RDD<Rating> ratings, int rank, int iterations, double lambda) Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) pairs.
static MatrixFactorizationModel	train(RDD<Rating> ratings, int rank, int iterations, double lambda, int blocks)
static MatrixFactorizationModel	train(RDD<Rating> ratings, int rank, int iterations, double lambda, int blocks, long seed)
static MatrixFactorizationModel	trainImplicit(RDD<Rating> ratings, int rank, int iterations) Train a matrix factorization model given an RDD of 'implicit preferences' ratings given by users to some products, in the form of (userID, productID, rating) pairs.
static MatrixFactorizationModel	trainImplicit(RDD<Rating> ratings, int rank, int iterations, double lambda, double alpha) 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.
static MatrixFactorizationModel	trainImplicit(RDD<Rating> ratings, int rank, int iterations, double lambda, int blocks, double alpha) 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.
static MatrixFactorizationModel	trainImplicit(RDD<Rating> ratings, int rank, int iterations, double lambda, int blocks, double alpha, long seed) 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.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

ALS

public ALS()

Method Detail

train

public static MatrixFactorizationModel train(RDD<Rating> ratings,
                                             int rank,
                                             int iterations,
                                             double lambda,
                                             int blocks,
                                             long seed)

train

public static MatrixFactorizationModel train(RDD<Rating> ratings,
                                             int rank,
                                             int iterations,
                                             double lambda,
                                             int blocks)

train

public static MatrixFactorizationModel train(RDD<Rating> ratings,
                                             int rank,
                                             int iterations,
                                             double lambda)

Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) 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. The level of parallelism is determined automatically based on the number of partitions in ratings.

Parameters:

ratings - RDD of (userID, productID, rating) pairs

rank - number of features to use

iterations - number of iterations of ALS (recommended: 10-20)

lambda - regularization factor (recommended: 0.01)

Returns:

(undocumented)

train

public static MatrixFactorizationModel train(RDD<Rating> ratings,
                                             int rank,
                                             int iterations)

Train a matrix factorization model given an RDD of ratings given by users to some products, in the form of (userID, productID, rating) 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. The level of parallelism is determined automatically based on the number of partitions in ratings.

Parameters:

ratings - RDD of (userID, productID, rating) pairs

rank - number of features to use

iterations - number of iterations of ALS (recommended: 10-20)

Returns:

(undocumented)

trainImplicit

public static MatrixFactorizationModel trainImplicit(RDD<Rating> ratings,
                                                     int rank,
                                                     int iterations,
                                                     double lambda,
                                                     int blocks,
                                                     double alpha,
                                                     long seed)

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.

Parameters:

ratings - RDD of (userID, productID, rating) pairs

rank - number of features to use

iterations - number of iterations of ALS (recommended: 10-20)

lambda - regularization factor (recommended: 0.01)

blocks - level of parallelism to split computation into

alpha - confidence parameter

seed - random seed

Returns:

(undocumented)

trainImplicit

public static MatrixFactorizationModel trainImplicit(RDD<Rating> ratings,
                                                     int rank,
                                                     int iterations,
                                                     double lambda,
                                                     int blocks,
                                                     double alpha)

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.

Parameters:

ratings - RDD of (userID, productID, rating) pairs

rank - number of features to use

iterations - number of iterations of ALS (recommended: 10-20)

lambda - regularization factor (recommended: 0.01)

blocks - level of parallelism to split computation into

alpha - confidence parameter

Returns:

(undocumented)

trainImplicit

public static MatrixFactorizationModel trainImplicit(RDD<Rating> ratings,
                                                     int rank,
                                                     int iterations,
                                                     double lambda,
                                                     double alpha)

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. The level of parallelism is determined automatically based on the number of partitions in ratings.

Parameters:

ratings - RDD of (userID, productID, rating) pairs

rank - number of features to use

iterations - number of iterations of ALS (recommended: 10-20)

lambda - regularization factor (recommended: 0.01)

alpha - confidence parameter

Returns:

(undocumented)

trainImplicit

public static MatrixFactorizationModel trainImplicit(RDD<Rating> ratings,
                                                     int rank,
                                                     int iterations)

Train a matrix factorization model given an RDD of 'implicit preferences' ratings given by users to some products, in the form of (userID, productID, rating) 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. The level of parallelism is determined automatically based on the number of partitions in ratings. Model parameters alpha and lambda are set to reasonable default values

Parameters:

ratings - RDD of (userID, productID, rating) pairs

rank - number of features to use

iterations - number of iterations of ALS (recommended: 10-20)

Returns:

(undocumented)

setBlocks

public ALS setBlocks(int numBlocks)

setUserBlocks

public ALS setUserBlocks(int numUserBlocks)

setProductBlocks

public ALS setProductBlocks(int numProductBlocks)

setRank

public ALS setRank(int rank)

setIterations

public ALS setIterations(int iterations)

setLambda

public ALS setLambda(double lambda)

setImplicitPrefs

public ALS setImplicitPrefs(boolean implicitPrefs)

setAlpha

public ALS setAlpha(double alpha)

setSeed

public ALS setSeed(long seed)

setNonnegative

public ALS setNonnegative(boolean b)

setIntermediateRDDStorageLevel

public ALS setIntermediateRDDStorageLevel(StorageLevel storageLevel)

setFinalRDDStorageLevel

public ALS setFinalRDDStorageLevel(StorageLevel storageLevel)

setCheckpointInterval

public ALS setCheckpointInterval(int checkpointInterval)

run

public MatrixFactorizationModel run(RDD<Rating> ratings)

run

public MatrixFactorizationModel run(JavaRDD<Rating> ratings)