org.apache.flink.ml.recommendation

Class ALS$

java.lang.Object

org.apache.flink.ml.recommendation.ALS$

public class ALS$
extends Object

Field Summary

Fields

Modifier and Type	Field and Description
static ALS$	MODULE$ Static reference to the singleton instance of this Scala object.

Constructor Summary

Constructors

Constructor and Description
ALS$()

Method Summary

Modifier and Type	Method and Description
ALS	apply()
scala.Tuple2<DataSet<scala.Tuple2<Object,ALS.InBlockInformation>>,DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>>>	createBlockInformation(int userBlocks, int itemBlocks, DataSet<scala.Tuple2<Object,ALS.Rating>> ratings, ALS.BlockIDPartitioner blockIDPartitioner) Creates the meta information needed to route the item and user vectors to the respective user and item blocks.
DataSet<scala.Tuple2<Object,ALS.InBlockInformation>>	createInBlockInformation(DataSet<scala.Tuple2<Object,ALS.Rating>> ratings, DataSet<scala.Tuple2<Object,int[]>> usersPerBlock, ALS.BlockIDGenerator blockIDGenerator) Creates the incoming block information
DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>>	createOutBlockInformation(DataSet<scala.Tuple2<Object,ALS.Rating>> ratings, DataSet<scala.Tuple2<Object,int[]>> usersPerBlock, int itemBlocks, ALS.BlockIDGenerator blockIDGenerator) Creates the outgoing block information
DataSet<scala.Tuple2<Object,int[]>>	createUsersPerBlock(DataSet<scala.Tuple2<Object,ALS.Rating>> ratings) Calculates the userIDs in ascending order of each user block
Object	fitALS() Calculates the matrix factorization for the given ratings.
void	generateFullMatrix(double[] triangularMatrix, double[] fullMatrix, int factors)
DataSet<ALS.Factors>	generateRandomMatrix(DataSet<Object> users, int factors, long seed)
String	ITEM_FACTORS_FILE()
void	outerProduct(double[] vector, double[] matrix, int factors)
Object	predictRating() Predict operation which calculates the matrix entry for the given indices
double[]	randomFactors(int factors, scala.util.Random random)
DataSet<ALS.Factors>	unblock(DataSet<scala.Tuple2<Object,double[][]>> users, DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>> outInfo, ALS.BlockIDPartitioner blockIDPartitioner) Unblocks the blocked user and item matrix representation so that it is at DataSet of column vectors.
DataSet<scala.Tuple2<Object,double[][]>>	updateFactors(int numUserBlocks, DataSet<scala.Tuple2<Object,double[][]>> items, DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>> itemOut, DataSet<scala.Tuple2<Object,ALS.InBlockInformation>> userIn, int factors, double lambda, Partitioner<Object> blockIDPartitioner) Calculates a single half step of the ALS optimization.
String	USER_FACTORS_FILE()

Methods inherited from class java.lang.Object

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

Field Detail

MODULE$

public static final ALS$ MODULE$

Static reference to the singleton instance of this Scala object.

Constructor Detail

ALS$

public ALS$()

Method Detail

USER_FACTORS_FILE

public String USER_FACTORS_FILE()

ITEM_FACTORS_FILE

public String ITEM_FACTORS_FILE()

apply

public ALS apply()

predictRating

public Object predictRating()

Predict operation which calculates the matrix entry for the given indices

fitALS

public Object fitALS()

Calculates the matrix factorization for the given ratings. A rating is defined as a tuple of user ID, item ID and the corresponding rating.

Returns:

Factorization containing the user and item matrix

updateFactors

public DataSet<scala.Tuple2<Object,double[][]>> updateFactors(int numUserBlocks,
                                                              DataSet<scala.Tuple2<Object,double[][]>> items,
                                                              DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>> itemOut,
                                                              DataSet<scala.Tuple2<Object,ALS.InBlockInformation>> userIn,
                                                              int factors,
                                                              double lambda,
                                                              Partitioner<Object> blockIDPartitioner)

Calculates a single half step of the ALS optimization. The result is the new value for either the user or item matrix, depending with which matrix the method was called.

Parameters:

numUserBlocks - Number of blocks in the respective dimension

items - Fixed matrix value for the half step

itemOut - Out information to know where to send the vectors

userIn - In information for the cogroup step

factors - Number of latent factors

lambda - Regularization constant

blockIDPartitioner - Custom Flink partitioner

Returns:

New value for the optimized matrix (either user or item)

createBlockInformation

public scala.Tuple2<DataSet<scala.Tuple2<Object,ALS.InBlockInformation>>,DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>>> createBlockInformation(int userBlocks,
                                                                                                                                                       int itemBlocks,
                                                                                                                                                       DataSet<scala.Tuple2<Object,ALS.Rating>> ratings,
                                                                                                                                                       ALS.BlockIDPartitioner blockIDPartitioner)

Creates the meta information needed to route the item and user vectors to the respective user and item blocks. * @param userBlocks

Parameters:

itemBlocks -

ratings -

blockIDPartitioner -

Returns:

createUsersPerBlock

public DataSet<scala.Tuple2<Object,int[]>> createUsersPerBlock(DataSet<scala.Tuple2<Object,ALS.Rating>> ratings)

Calculates the userIDs in ascending order of each user block

Parameters:

ratings -

Returns:

createOutBlockInformation

public DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>> createOutBlockInformation(DataSet<scala.Tuple2<Object,ALS.Rating>> ratings,
                                                                                       DataSet<scala.Tuple2<Object,int[]>> usersPerBlock,
                                                                                       int itemBlocks,
                                                                                       ALS.BlockIDGenerator blockIDGenerator)

Creates the outgoing block information

Creates for every user block the outgoing block information. The out block information contains for every item block a BitSet which indicates which user vector has to be sent to this block. If a vector v has to be sent to a block b, then bitsets(b)'s bit v is set to 1, otherwise 0. Additionally the user IDataSet are replaced by the user vector's index value.

Parameters:

ratings -

usersPerBlock -

itemBlocks -

blockIDGenerator -

Returns:

createInBlockInformation

public DataSet<scala.Tuple2<Object,ALS.InBlockInformation>> createInBlockInformation(DataSet<scala.Tuple2<Object,ALS.Rating>> ratings,
                                                                                     DataSet<scala.Tuple2<Object,int[]>> usersPerBlock,
                                                                                     ALS.BlockIDGenerator blockIDGenerator)

Creates the incoming block information

Creates for every user block the incoming block information. The incoming block information contains the userIDs of the users in the respective block and for every item block a BlockRating instance. The BlockRating instance describes for every incoming set of item vectors of an item block, which user rated these items and what the rating was. For that purpose it contains for every incoming item vector a tuple of an id array us and a rating array rs. The array us contains the indices of the users having rated the respective item vector with the ratings in rs.

Parameters:

ratings -

usersPerBlock -

blockIDGenerator -

Returns:

unblock

public DataSet<ALS.Factors> unblock(DataSet<scala.Tuple2<Object,double[][]>> users,
                                    DataSet<scala.Tuple2<Object,ALS.OutBlockInformation>> outInfo,
                                    ALS.BlockIDPartitioner blockIDPartitioner)

Unblocks the blocked user and item matrix representation so that it is at DataSet of column vectors.

Parameters:

users -

outInfo -

blockIDPartitioner -

Returns:

outerProduct

public void outerProduct(double[] vector,
                         double[] matrix,
                         int factors)

generateFullMatrix

public void generateFullMatrix(double[] triangularMatrix,
                               double[] fullMatrix,
                               int factors)

generateRandomMatrix

public DataSet<ALS.Factors> generateRandomMatrix(DataSet<Object> users,
                                                 int factors,
                                                 long seed)

randomFactors

public double[] randomFactors(int factors,
                              scala.util.Random random)