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, Logging, Params, Identifiable, MLWritable

public class ALSModel
extends Model<ALSModel>
implements 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

Method Summary

Modifier and Type	Method and Description
UserDefinedFunction	checkedCast() Attempts to safely cast a user/item id to an Int.
static Params	clear(Param<?> param)
static Param<String>	coldStartStrategy()
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.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static <T> scala.Option<T>	get(Param<T> param)
static String	getColdStartStrategy()
String	getColdStartStrategy()
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getItemCol()
String	getItemCol()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
static String	getUserCol()
String	getUserCol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	itemCol()
Param<String>	itemCol() Param for the column name for item ids.
Dataset<Row>	itemFactors()
static ALSModel	load(String path)
static Param<?>[]	params()
static void	parent_$eq(Estimator<M> x$1)
static Estimator<M>	parent()
static Param<String>	predictionCol()
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.
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
ALSModel	setColdStartStrategy(String value)
ALSModel	setItemCol(String value)
static M	setParent(Estimator<M> parent)
ALSModel	setPredictionCol(String value)
ALSModel	setUserCol(String value)
static String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
static Param<String>	userCol()
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 Object

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

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

getPredictionCol, predictionCol

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, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

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

toString

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

save

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

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

Method Detail

read

public static MLReader<ALSModel> read()

load

public static ALSModel load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
                                   T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

parent

public static Estimator<M> parent()

parent_$eq

public static void parent_$eq(Estimator<M> x$1)

setParent

public static M setParent(Estimator<M> parent)

hasParent

public static boolean hasParent()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

userCol

public static Param<String> userCol()

getUserCol

public static String getUserCol()

itemCol

public static Param<String> itemCol()

getItemCol

public static String getItemCol()

coldStartStrategy

public static Param<String> coldStartStrategy()

getColdStartStrategy

public static String getColdStartStrategy()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

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)

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

:: DeveloperApi ::

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)

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.

checkedCast

public UserDefinedFunction checkedCast()

Attempts to safely cast a user/item id to an Int. Throws an exception if the value is out of integer range or contains a fractional part.

Returns:

(undocumented)

coldStartStrategy

public Param<String> coldStartStrategy()

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".

Returns:

(undocumented)

getColdStartStrategy

public String getColdStartStrategy()

getItemCol

public String getItemCol()

getUserCol

public String getUserCol()

itemCol

public Param<String> itemCol()

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"

Returns:

(undocumented)

userCol

public Param<String> userCol()

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"

Returns:

(undocumented)