Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package recommendation
    Definition Classes
    ml
  • ALS
  • ALSModel

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
Linear Supertypes
DefaultParamsWritable, MLWritable, ALSParams, HasSeed, HasCheckpointInterval, HasRegParam, HasMaxIter, ALSModelParams, HasBlockSize, HasPredictionCol, Estimator[ALSModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ALS
  2. DefaultParamsWritable
  3. MLWritable
  4. ALSParams
  5. HasSeed
  6. HasCheckpointInterval
  7. HasRegParam
  8. HasMaxIter
  9. ALSModelParams
  10. HasBlockSize
  11. HasPredictionCol
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ALS()
    Annotations
    @Since( "1.4.0" )
  2. new ALS(uid: String)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. 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
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. 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
  8. def checkIntegers(dataset: Dataset[_], colName: String): Column

    Attempts to safely cast a user/item id to an Int.

    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.

    Attributes
    protected[recommendation]
    Definition Classes
    ALSModelParams
  9. 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
  10. 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
  11. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  12. 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
  13. 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" )
  14. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  15. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  18. 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
  19. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  20. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  21. 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
  22. 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
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def fit(dataset: Dataset[_]): ALSModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    ALS → Estimator
    Annotations
    @Since( "2.0.0" )
  25. 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" )
  26. 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" )
  27. 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()
  28. 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
  29. def getAlpha: Double

    Definition Classes
    ALSParams
  30. final def getBlockSize: Int

    Definition Classes
    HasBlockSize
  31. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  32. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  33. def getColdStartStrategy: String

    Definition Classes
    ALSModelParams
  34. 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
  35. def getFinalStorageLevel: String

    Definition Classes
    ALSParams
  36. def getImplicitPrefs: Boolean

    Definition Classes
    ALSParams
  37. def getIntermediateStorageLevel: String

    Definition Classes
    ALSParams
  38. def getItemCol: String

    Definition Classes
    ALSModelParams
  39. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  40. def getNonnegative: Boolean

    Definition Classes
    ALSParams
  41. def getNumItemBlocks: Int

    Definition Classes
    ALSParams
  42. def getNumUserBlocks: Int

    Definition Classes
    ALSParams
  43. 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
  44. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  45. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  46. def getRank: Int

    Definition Classes
    ALSParams
  47. def getRatingCol: String

    Definition Classes
    ALSParams
  48. final def getRegParam: Double

    Definition Classes
    HasRegParam
  49. final def getSeed: Long

    Definition Classes
    HasSeed
  50. def getUserCol: String

    Definition Classes
    ALSModelParams
  51. 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
  52. 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
  53. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  54. val implicitPrefs: BooleanParam

    Param to decide whether to use implicit preference.

    Param to decide whether to use implicit preference. Default: false

    Definition Classes
    ALSParams
  55. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  56. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. 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
  58. 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
  59. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  60. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  61. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  62. 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
  63. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  76. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  77. val nonnegative: BooleanParam

    Param for whether to apply nonnegativity constraints.

    Param for whether to apply nonnegativity constraints. Default: false

    Definition Classes
    ALSParams
  78. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  79. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  80. val numItemBlocks: IntParam

    Param for number of item blocks (positive).

    Param for number of item blocks (positive). Default: 10

    Definition Classes
    ALSParams
  81. val numUserBlocks: IntParam

    Param for number of user blocks (positive).

    Param for number of user blocks (positive). Default: 10

    Definition Classes
    ALSParams
  82. 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.

  83. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  84. val rank: IntParam

    Param for rank of the matrix factorization (positive).

    Param for rank of the matrix factorization (positive). Default: 10

    Definition Classes
    ALSParams
  85. val ratingCol: Param[String]

    Param for the column name for ratings.

    Param for the column name for ratings. Default: "rating"

    Definition Classes
    ALSParams
  86. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  87. 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( ... )
  88. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  89. final def set(paramPair: ParamPair[_]): ALS.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  90. final def set(param: String, value: Any): ALS.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  91. 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
  92. def setAlpha(value: Double): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  93. 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" )
  94. def setCheckpointInterval(value: Int): ALS.this.type

    Annotations
    @Since( "1.4.0" )
  95. def setColdStartStrategy(value: String): ALS.this.type

    Annotations
    @Since( "2.2.0" )
  96. final def setDefault(paramPairs: ParamPair[_]*): ALS.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  97. final def setDefault[T](param: Param[T], value: T): ALS.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected[ml]
    Definition Classes
    Params
  98. def setFinalStorageLevel(value: String): ALS.this.type

    Annotations
    @Since( "2.0.0" )
  99. def setImplicitPrefs(value: Boolean): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  100. def setIntermediateStorageLevel(value: String): ALS.this.type

    Annotations
    @Since( "2.0.0" )
  101. def setItemCol(value: String): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  102. def setMaxIter(value: Int): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  103. def setNonnegative(value: Boolean): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  104. 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" )
  105. def setNumItemBlocks(value: Int): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  106. def setNumUserBlocks(value: Int): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  107. def setPredictionCol(value: String): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  108. def setRank(value: Int): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  109. def setRatingCol(value: String): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  110. def setRegParam(value: Double): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  111. def setSeed(value: Long): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  112. def setUserCol(value: String): ALS.this.type

    Annotations
    @Since( "1.3.0" )
  113. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  114. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  115. 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 by Param.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" )
  116. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  117. 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" )
  118. 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
  119. def validateAndTransformSchema(schema: StructType): StructType

    Validates and transforms the input schema.

    Validates and transforms the input schema.

    schema

    input schema

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ALSParams
  120. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  121. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  122. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  123. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from ALSParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from HasRegParam

Inherited from HasMaxIter

Inherited from ALSModelParams

Inherited from HasBlockSize

Inherited from HasPredictionCol

Inherited from Estimator[ALSModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(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.

(expert-only) Parameter setters

(expert-only) Parameter getters