public class ALS extends Estimator<ALSModel>
ALS attempts to estimate the ratings matrix R as the product of two lower-rank matrices,
X and Y, i.e. X * Yt = R. Typically these approximations are called 'factor' matrices.
The general approach is iterative. During each iteration, one of the factor matrices is held
constant, while the other is solved for using least squares. The newly-solved factor matrix is
then held constant while solving for the other factor matrix.
This is a blocked implementation of the ALS factorization algorithm that groups the two sets of factors (referred to as "users" and "products") into blocks and reduces communication by only sending one copy of each user vector to each product block on each iteration, and only for the product blocks that need that user's feature vector. This is achieved by pre-computing some information about the ratings matrix to determine the "out-links" of each user (which blocks of products it will contribute to) and "in-link" information for each product (which of the feature vectors it receives from each user block it will depend on). This allows us to send only an array of feature vectors between each user block and product block, and have the product block find the users' ratings and update the products based on these messages.
For implicit preference data, the algorithm used is based on
"Collaborative Filtering for Implicit Feedback Datasets", available at
http://dx.doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here.
Essentially instead of finding the low-rank approximations to the rating matrix R,
this finds the approximations for a preference matrix P where the elements of P are 1 if
r > 0 and 0 if r <= 0. The ratings then act as 'confidence' values related to strength of
indicated user
preferences rather than explicit ratings given to items.
| Modifier and Type | Class and Description |
|---|---|
static class |
ALS.Rating<ID>
:: DeveloperApi ::
Rating class for better code readability.
|
static class |
ALS.Rating$ |
| Modifier and Type | Method and Description |
|---|---|
DoubleParam |
alpha()
Param for the alpha parameter in the implicit preference formulation (>= 0).
|
ALS |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
ALSModel |
fit(DataFrame dataset)
Fits a model to the input data.
|
double |
getAlpha() |
boolean |
getImplicitPrefs() |
java.lang.String |
getItemCol() |
boolean |
getNonnegative() |
int |
getNumItemBlocks() |
int |
getNumUserBlocks() |
int |
getRank() |
java.lang.String |
getRatingCol() |
java.lang.String |
getUserCol() |
BooleanParam |
implicitPrefs()
Param to decide whether to use implicit preference.
|
Param<java.lang.String> |
itemCol()
Param for the column name for item ids.
|
static ALS |
load(java.lang.String path) |
BooleanParam |
nonnegative()
Param for whether to apply nonnegativity constraints.
|
IntParam |
numItemBlocks()
Param for number of item blocks (>= 1).
|
IntParam |
numUserBlocks()
Param for number of user blocks (>= 1).
|
IntParam |
rank()
Param for rank of the matrix factorization (>= 1).
|
Param<java.lang.String> |
ratingCol()
Param for the column name for ratings.
|
ALS |
setAlpha(double value) |
ALS |
setCheckpointInterval(int value) |
ALS |
setImplicitPrefs(boolean value) |
ALS |
setItemCol(java.lang.String value) |
ALS |
setMaxIter(int value) |
ALS |
setNonnegative(boolean value) |
ALS |
setNumBlocks(int value)
Sets both numUserBlocks and numItemBlocks to the specific value.
|
ALS |
setNumItemBlocks(int value) |
ALS |
setNumUserBlocks(int value) |
ALS |
setPredictionCol(java.lang.String value) |
ALS |
setRank(int value) |
ALS |
setRatingCol(java.lang.String value) |
ALS |
setRegParam(double value) |
ALS |
setSeed(long value) |
ALS |
setUserCol(java.lang.String value) |
static <ID> scala.Tuple2<RDD<scala.Tuple2<ID,float[]>>,RDD<scala.Tuple2<ID,float[]>>> |
train(RDD<ALS.Rating<ID>> ratings,
int rank,
int numUserBlocks,
int numItemBlocks,
int maxIter,
double regParam,
boolean implicitPrefs,
double alpha,
boolean nonnegative,
StorageLevel intermediateRDDStorageLevel,
StorageLevel finalRDDStorageLevel,
int checkpointInterval,
long seed,
scala.reflect.ClassTag<ID> evidence$1,
scala.math.Ordering<ID> ord) |
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
java.lang.String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<java.lang.String> |
userCol()
Param for the column name for user ids.
|
StructType |
validateAndTransformSchema(StructType schema)
Validates and transforms the input schema.
|
transformSchemaclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParamstoStringinitializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static ALS load(java.lang.String path)
public static <ID> scala.Tuple2<RDD<scala.Tuple2<ID,float[]>>,RDD<scala.Tuple2<ID,float[]>>> train(RDD<ALS.Rating<ID>> ratings, int rank, int numUserBlocks, int numItemBlocks, int maxIter, double regParam, boolean implicitPrefs, double alpha, boolean nonnegative, StorageLevel intermediateRDDStorageLevel, StorageLevel finalRDDStorageLevel, int checkpointInterval, long seed, scala.reflect.ClassTag<ID> evidence$1, scala.math.Ordering<ID> ord)
public java.lang.String uid()
Identifiableuid in interface Identifiablepublic ALS setRank(int value)
public ALS setNumUserBlocks(int value)
public ALS setNumItemBlocks(int value)
public ALS setImplicitPrefs(boolean value)
public ALS setAlpha(double value)
public ALS setUserCol(java.lang.String value)
public ALS setItemCol(java.lang.String value)
public ALS setRatingCol(java.lang.String value)
public ALS setPredictionCol(java.lang.String value)
public ALS setMaxIter(int value)
public ALS setRegParam(double value)
public ALS setNonnegative(boolean value)
public ALS setCheckpointInterval(int value)
public ALS setSeed(long value)
public ALS setNumBlocks(int value)
value - (undocumented)public ALSModel fit(DataFrame dataset)
Estimatorpublic StructType transformSchema(StructType schema)
PipelineStageDerives the output schema from the input schema.
transformSchema in class PipelineStageschema - (undocumented)public ALS copy(ParamMap extra)
Paramspublic IntParam rank()
public int getRank()
public IntParam numUserBlocks()
public int getNumUserBlocks()
public IntParam numItemBlocks()
public int getNumItemBlocks()
public BooleanParam implicitPrefs()
public boolean getImplicitPrefs()
public DoubleParam alpha()
public double getAlpha()
public Param<java.lang.String> ratingCol()
public java.lang.String getRatingCol()
public BooleanParam nonnegative()
public boolean getNonnegative()
public StructType validateAndTransformSchema(StructType schema)
schema - input schemapublic Param<java.lang.String> userCol()
public java.lang.String getUserCol()
public Param<java.lang.String> itemCol()
public java.lang.String getItemCol()