class ALS extends Estimator[ALSModel] with ALSParams with DefaultParamsWritable
Alternating Least Squares (ALS) matrix factorization.
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 https://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 is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence'
values related to strength of indicated user
preferences rather than explicit ratings given to items.
Note: the input rating dataset to the ALS implementation should be deterministic.
Nondeterministic data can cause failure during fitting ALS model.
For example, an order-sensitive operation like sampling after a repartition makes dataset
output nondeterministic, like dataset.repartition(2).sample(false, 0.5, 1618)
.
Checkpointing sampled dataset or adding a sort before sampling can help make the dataset
deterministic.
- Annotations
- @Since( "1.3.0" )
- Source
- ALS.scala
- Grouped
- Alphabetic
- By Inheritance
- ALS
- DefaultParamsWritable
- MLWritable
- ALSParams
- HasSeed
- HasCheckpointInterval
- HasRegParam
- HasMaxIter
- ALSModelParams
- HasBlockSize
- HasPredictionCol
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
val
alpha: DoubleParam
Param for the alpha parameter in the implicit preference formulation (nonnegative).
Param for the alpha parameter in the implicit preference formulation (nonnegative). Default: 1.0
- Definition Classes
- ALSParams
-
final
val
checkpointInterval: IntParam
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.
- Definition Classes
- HasCheckpointInterval
-
val
implicitPrefs: BooleanParam
Param to decide whether to use implicit preference.
Param to decide whether to use implicit preference. Default: false
- Definition Classes
- ALSParams
-
val
itemCol: Param[String]
Param for the column name for item ids.
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"
- Definition Classes
- ALSModelParams
-
final
val
maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
-
val
nonnegative: BooleanParam
Param for whether to apply nonnegativity constraints.
Param for whether to apply nonnegativity constraints. Default: false
- Definition Classes
- ALSParams
-
val
numItemBlocks: IntParam
Param for number of item blocks (positive).
Param for number of item blocks (positive). Default: 10
- Definition Classes
- ALSParams
-
val
numUserBlocks: IntParam
Param for number of user blocks (positive).
Param for number of user blocks (positive). Default: 10
- Definition Classes
- ALSParams
-
final
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
-
val
rank: IntParam
Param for rank of the matrix factorization (positive).
Param for rank of the matrix factorization (positive). Default: 10
- Definition Classes
- ALSParams
-
val
ratingCol: Param[String]
Param for the column name for ratings.
Param for the column name for ratings. Default: "rating"
- Definition Classes
- ALSParams
-
final
val
regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).
- Definition Classes
- HasRegParam
-
final
val
seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
-
val
userCol: Param[String]
Param for the column name for user ids.
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"
- Definition Classes
- ALSModelParams
Members
-
final
def
clear(param: Param[_]): ALS.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
copy(extra: ParamMap): ALS
Creates a copy of this instance with the same UID and some extra 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()
.- Definition Classes
- ALS → Estimator → PipelineStage → Params
- Annotations
- @Since( "1.5.0" )
-
def
explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
-
def
fit(dataset: Dataset[_]): ALSModel
Fits a model to the input data.
-
def
fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[ALSModel]
Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.
- dataset
input dataset
- paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted models, matching the input parameter maps
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], paramMap: ParamMap): ALSModel
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
- dataset
input dataset
- paramMap
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): ALSModel
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
- dataset
input dataset
- firstParamPair
the first param pair, overrides embedded params
- otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
-
final
def
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
final
def
getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
final
def
hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
-
final
def
isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
lazy val
params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
-
def
save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path)
.Saves this ML instance to the input path, a shortcut of
write.save(path)
.- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): ALS.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- ALS → PipelineStage
- Annotations
- @Since( "1.3.0" )
-
val
uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- ALS → Identifiable
- Annotations
- @Since( "1.4.0" )
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Parameter setters
-
def
setAlpha(value: Double): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setCheckpointInterval(value: Int): ALS.this.type
- Annotations
- @Since( "1.4.0" )
-
def
setImplicitPrefs(value: Boolean): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setItemCol(value: String): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setMaxIter(value: Int): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setNonnegative(value: Boolean): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setNumBlocks(value: Int): ALS.this.type
Sets both numUserBlocks and numItemBlocks to the specific value.
Sets both numUserBlocks and numItemBlocks to the specific value.
- Annotations
- @Since( "1.3.0" )
-
def
setNumItemBlocks(value: Int): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setNumUserBlocks(value: Int): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setPredictionCol(value: String): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setRank(value: Int): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setRatingCol(value: String): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setRegParam(value: Double): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setSeed(value: Long): ALS.this.type
- Annotations
- @Since( "1.3.0" )
-
def
setUserCol(value: String): ALS.this.type
- Annotations
- @Since( "1.3.0" )
Parameter getters
-
def
getAlpha: Double
- Definition Classes
- ALSParams
-
final
def
getCheckpointInterval: Int
- Definition Classes
- HasCheckpointInterval
-
def
getImplicitPrefs: Boolean
- Definition Classes
- ALSParams
-
def
getItemCol: String
- Definition Classes
- ALSModelParams
-
final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
-
def
getNonnegative: Boolean
- Definition Classes
- ALSParams
-
def
getNumItemBlocks: Int
- Definition Classes
- ALSParams
-
def
getNumUserBlocks: Int
- Definition Classes
- ALSParams
-
final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
-
def
getRank: Int
- Definition Classes
- ALSParams
-
def
getRatingCol: String
- Definition Classes
- ALSParams
-
final
def
getRegParam: Double
- Definition Classes
- HasRegParam
-
final
def
getSeed: Long
- Definition Classes
- HasSeed
-
def
getUserCol: String
- Definition Classes
- ALSModelParams
(expert-only) Parameters
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
final
val
blockSize: IntParam
Param for block size for stacking input data in matrices.
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..
- Definition Classes
- HasBlockSize
-
val
coldStartStrategy: Param[String]
Param for strategy for dealing with unknown or new users/items at prediction time.
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".
- Definition Classes
- ALSModelParams
-
val
finalStorageLevel: Param[String]
Param for StorageLevel for ALS model factors.
Param for StorageLevel for ALS model factors. Pass in a string representation of
StorageLevel
. Default: "MEMORY_AND_DISK".- Definition Classes
- ALSParams
-
val
intermediateStorageLevel: Param[String]
Param for StorageLevel for intermediate datasets.
Param for StorageLevel for intermediate datasets. Pass in a string representation of
StorageLevel
. Cannot be "NONE". Default: "MEMORY_AND_DISK".- Definition Classes
- ALSParams
(expert-only) Parameter setters
-
def
setBlockSize(value: Int): ALS.this.type
Set block size for stacking input data in matrices.
Set block size for stacking input data in matrices. Default is 4096.
- Annotations
- @Since( "3.0.0" )
-
def
setColdStartStrategy(value: String): ALS.this.type
- Annotations
- @Since( "2.2.0" )
-
def
setFinalStorageLevel(value: String): ALS.this.type
- Annotations
- @Since( "2.0.0" )
-
def
setIntermediateStorageLevel(value: String): ALS.this.type
- Annotations
- @Since( "2.0.0" )
(expert-only) Parameter getters
-
final
def
getBlockSize: Int
- Definition Classes
- HasBlockSize
-
def
getColdStartStrategy: String
- Definition Classes
- ALSModelParams
-
def
getFinalStorageLevel: String
- Definition Classes
- ALSParams
-
def
getIntermediateStorageLevel: String
- Definition Classes
- ALSParams