org.apache.spark.ml.recommendation

Class ALSModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<ALSModel>

org.apache.spark.ml.recommendation.ALSModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, Params, HasBlockSize, HasPredictionCol, ALSModelParams, Identifiable, MLWritable

public class ALSModel
extends Model<ALSModel>
implements ALSModelParams, MLWritable

Model fitted by ALS.

param: rank rank of the matrix factorization model param: userFactors a DataFrame that stores user factors in two columns: id and features param: itemFactors a DataFrame that stores item factors in two columns: id and features

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method and Description
IntParam	blockSize() Param for block size for stacking input data in matrices.
Param<String>	coldStartStrategy() Param for strategy for dealing with unknown or new users/items at prediction time.
ALSModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	itemCol() Param for the column name for item ids.
Dataset<Row>	itemFactors()
static ALSModel	load(String path)
Param<String>	predictionCol() Param for prediction column name.
int	rank()
static MLReader<ALSModel>	read()
Dataset<Row>	recommendForAllItems(int numUsers) Returns top numUsers users recommended for each item, for all items.
Dataset<Row>	recommendForAllUsers(int numItems) Returns top numItems items recommended for each user, for all users.
Dataset<Row>	recommendForItemSubset(Dataset<?> dataset, int numUsers) Returns top numUsers users recommended for each item id in the input data set.
Dataset<Row>	recommendForUserSubset(Dataset<?> dataset, int numItems) Returns top numItems items recommended for each user id in the input data set.
ALSModel	setBlockSize(int value) Set block size for stacking input data in matrices.
ALSModel	setColdStartStrategy(String value)
ALSModel	setItemCol(String value)
ALSModel	setPredictionCol(String value)
ALSModel	setUserCol(String value)
String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
Param<String>	userCol() Param for the column name for user ids.
Dataset<Row>	userFactors()
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.recommendation.ALSModelParams

checkIntegers, getColdStartStrategy, getItemCol, getUserCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasBlockSize

getBlockSize

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Method Detail

read

public static MLReader<ALSModel> read()

load

public static ALSModel load(String path)

userCol

public Param<String> userCol()

Description copied from interface: ALSModelParams

Param for the column name for user ids. Ids must be integers. Other numeric types are supported for this column, but will be cast to integers as long as they fall within the integer value range. Default: "user"

Specified by:

userCol in interface ALSModelParams

Returns:

(undocumented)

itemCol

public Param<String> itemCol()

Description copied from interface: ALSModelParams

Param for the column name for item ids. Ids must be integers. Other numeric types are supported for this column, but will be cast to integers as long as they fall within the integer value range. Default: "item"

Specified by:

itemCol in interface ALSModelParams

Returns:

(undocumented)

coldStartStrategy

public Param<String> coldStartStrategy()

Description copied from interface: ALSModelParams

Param for strategy for dealing with unknown or new users/items at prediction time. This may be useful in cross-validation or production scenarios, for handling user/item ids the model has not seen in the training data. Supported values: - "nan": predicted value for unknown ids will be NaN. - "drop": rows in the input DataFrame containing unknown ids will be dropped from the output DataFrame containing predictions. Default: "nan".

Specified by:

coldStartStrategy in interface ALSModelParams

Returns:

(undocumented)

blockSize

public final IntParam blockSize()

Description copied from interface: HasBlockSize

Param for block size for stacking input data in matrices. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data..

Specified by:

blockSize in interface HasBlockSize

Returns:

(undocumented)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

rank

public int rank()

userFactors

public Dataset<Row> userFactors()

itemFactors

public Dataset<Row> itemFactors()

setUserCol

public ALSModel setUserCol(String value)

setItemCol

public ALSModel setItemCol(String value)

setPredictionCol

public ALSModel setPredictionCol(String value)

setColdStartStrategy

public ALSModel setColdStartStrategy(String value)

setBlockSize

public ALSModel setBlockSize(int value)

Set block size for stacking input data in matrices. Default is 4096.

Parameters:

value - (undocumented)

Returns:

(undocumented)

transform

public Dataset<Row> transform(Dataset<?> dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public ALSModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Model<ALSModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object

recommendForAllUsers

public Dataset<Row> recommendForAllUsers(int numItems)

Returns top numItems items recommended for each user, for all users.

Parameters:

numItems - max number of recommendations for each user

Returns:

a DataFrame of (userCol: Int, recommendations), where recommendations are stored as an array of (itemCol: Int, rating: Float) Rows.

recommendForUserSubset

public Dataset<Row> recommendForUserSubset(Dataset<?> dataset,
                                           int numItems)

Returns top numItems items recommended for each user id in the input data set. Note that if there are duplicate ids in the input dataset, only one set of recommendations per unique id will be returned.

Parameters:

dataset - a Dataset containing a column of user ids. The column name must match userCol.

numItems - max number of recommendations for each user.

Returns:

a DataFrame of (userCol: Int, recommendations), where recommendations are stored as an array of (itemCol: Int, rating: Float) Rows.

recommendForAllItems

public Dataset<Row> recommendForAllItems(int numUsers)

Returns top numUsers users recommended for each item, for all items.

Parameters:

numUsers - max number of recommendations for each item

Returns:

a DataFrame of (itemCol: Int, recommendations), where recommendations are stored as an array of (userCol: Int, rating: Float) Rows.

recommendForItemSubset

public Dataset<Row> recommendForItemSubset(Dataset<?> dataset,
                                           int numUsers)

Returns top numUsers users recommended for each item id in the input data set. Note that if there are duplicate ids in the input dataset, only one set of recommendations per unique id will be returned.

Parameters:

dataset - a Dataset containing a column of item ids. The column name must match itemCol.

numUsers - max number of recommendations for each item.

Returns:

a DataFrame of (itemCol: Int, recommendations), where recommendations are stored as an array of (userCol: Int, rating: Float) Rows.