Packages

class FMRegressor extends Regressor[Vector, FMRegressor, FMRegressionModel] with FactorizationMachines with FMRegressorParams with DefaultParamsWritable with Logging

Factorization Machines learning algorithm for regression. It supports normal gradient descent and AdamW solver.

The implementation is based upon: S. Rendle. "Factorization machines" 2010.

FM is able to estimate interactions even in problems with huge sparsity (like advertising and recommendation system). FM formula is:

$$ \begin{align} y = w_0 + \sum\limits^n_{i-1} w_i x_i + \sum\limits^n_{i=1} \sum\limits^n_{j=i+1} \langle v_i, v_j \rangle x_i x_j \end{align} $$
First two terms denote global bias and linear term (as same as linear regression), and last term denotes pairwise interactions term. v_i describes the i-th variable with k factors.

FM regression model uses MSE loss which can be solved by gradient descent method, and regularization terms like L2 are usually added to the loss function to prevent overfitting.

Annotations
@Since( "3.0.0" )
Source
FMRegressor.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, FMRegressorParams, FactorizationMachines, FactorizationMachinesParams, HasWeightCol, HasRegParam, HasFitIntercept, HasSeed, HasSolver, HasTol, HasStepSize, HasMaxIter, Regressor[Vector, FMRegressor, FMRegressionModel], Predictor[Vector, FMRegressor, FMRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[FMRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. FMRegressor
  2. DefaultParamsWritable
  3. MLWritable
  4. FMRegressorParams
  5. FactorizationMachines
  6. FactorizationMachinesParams
  7. HasWeightCol
  8. HasRegParam
  9. HasFitIntercept
  10. HasSeed
  11. HasSolver
  12. HasTol
  13. HasStepSize
  14. HasMaxIter
  15. Regressor
  16. Predictor
  17. PredictorParams
  18. HasPredictionCol
  19. HasFeaturesCol
  20. HasLabelCol
  21. Estimator
  22. PipelineStage
  23. Logging
  24. Params
  25. Serializable
  26. Serializable
  27. Identifiable
  28. AnyRef
  29. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new FMRegressor()
    Annotations
    @Since( "3.0.0" )
  2. new FMRegressor(uid: String)
    Annotations
    @Since( "3.0.0" )

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): FMRegressor.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def copy(extra: ParamMap): FMRegressor

    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
    FMRegressor → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "3.0.0" )
  9. 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
  10. 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
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. 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
  14. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  16. 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
  17. final val factorSize: IntParam

    Param for dimensionality of the factors (>= 0)

    Param for dimensionality of the factors (>= 0)

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  18. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  19. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. def fit(dataset: Dataset[_]): FMRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    Predictor → Estimator
  21. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[FMRegressionModel]

    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" )
  22. def fit(dataset: Dataset[_], paramMap: ParamMap): FMRegressionModel

    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" )
  23. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): FMRegressionModel

    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()
  24. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  25. final val fitLinear: BooleanParam

    Param for whether to fit linear term (aka 1-way term)

    Param for whether to fit linear term (aka 1-way term)

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  26. 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
  27. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  28. 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
  29. final def getFactorSize: Int

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  30. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  31. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  32. final def getFitLinear: Boolean

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  33. final def getInitStd: Double

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  34. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  35. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  36. final def getMiniBatchFraction: Double

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  37. 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
  38. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  39. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  40. final def getRegParam: Double

    Definition Classes
    HasRegParam
  41. final def getSeed: Long

    Definition Classes
    HasSeed
  42. final def getSolver: String

    Definition Classes
    HasSolver
  43. final def getStepSize: Double

    Definition Classes
    HasStepSize
  44. final def getTol: Double

    Definition Classes
    HasTol
  45. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  46. 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
  47. 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
  48. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. final val initStd: DoubleParam

    Param for standard deviation of initial coefficients

    Param for standard deviation of initial coefficients

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  50. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. 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
  53. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  54. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  55. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  56. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  57. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  58. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  65. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  70. final val miniBatchFraction: DoubleParam

    Param for mini-batch fraction, must be in range (0, 1]

    Param for mini-batch fraction, must be in range (0, 1]

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  71. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  72. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  73. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  74. 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.

  75. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  76. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  77. 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( ... )
  78. final val seed: LongParam

    Param for random seed.

    Param for random seed.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  80. final def set(param: String, value: Any): FMRegressor.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
  81. final def set[T](param: Param[T], value: T): FMRegressor.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  82. final def setDefault(paramPairs: ParamPair[_]*): FMRegressor.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
  83. final def setDefault[T](param: Param[T], value: T): FMRegressor.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
  84. def setFactorSize(value: Int): FMRegressor.this.type

    Set the dimensionality of the factors.

    Set the dimensionality of the factors. Default is 8.

    Annotations
    @Since( "3.0.0" )
  85. def setFeaturesCol(value: String): FMRegressor

    Definition Classes
    Predictor
  86. def setFitIntercept(value: Boolean): FMRegressor.this.type

    Set whether to fit intercept term.

    Set whether to fit intercept term. Default is true.

    Annotations
    @Since( "3.0.0" )
  87. def setFitLinear(value: Boolean): FMRegressor.this.type

    Set whether to fit linear term.

    Set whether to fit linear term. Default is true.

    Annotations
    @Since( "3.0.0" )
  88. def setInitStd(value: Double): FMRegressor.this.type

    Set the standard deviation of initial coefficients.

    Set the standard deviation of initial coefficients. Default is 0.01.

    Annotations
    @Since( "3.0.0" )
  89. def setLabelCol(value: String): FMRegressor

    Definition Classes
    Predictor
  90. def setMaxIter(value: Int): FMRegressor.this.type

    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

    Annotations
    @Since( "3.0.0" )
  91. def setMiniBatchFraction(value: Double): FMRegressor.this.type

    Set the mini-batch fraction parameter.

    Set the mini-batch fraction parameter. Default is 1.0.

    Annotations
    @Since( "3.0.0" )
  92. def setPredictionCol(value: String): FMRegressor

    Definition Classes
    Predictor
  93. def setRegParam(value: Double): FMRegressor.this.type

    Set the L2 regularization parameter.

    Set the L2 regularization parameter. Default is 0.0.

    Annotations
    @Since( "3.0.0" )
  94. def setSeed(value: Long): FMRegressor.this.type

    Set the random seed for weight initialization.

    Set the random seed for weight initialization.

    Annotations
    @Since( "3.0.0" )
  95. def setSolver(value: String): FMRegressor.this.type

    Set the solver algorithm used for optimization.

    Set the solver algorithm used for optimization. Supported options: "gd", "adamW". Default: "adamW"

    Annotations
    @Since( "3.0.0" )
  96. def setStepSize(value: Double): FMRegressor.this.type

    Set the initial step size for the first step (like learning rate).

    Set the initial step size for the first step (like learning rate). Default is 1.0.

    Annotations
    @Since( "3.0.0" )
  97. def setTol(value: Double): FMRegressor.this.type

    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Default is 1E-6.

    Annotations
    @Since( "3.0.0" )
  98. final val solver: Param[String]

    The solver algorithm for optimization.

    The solver algorithm for optimization. Supported options: "gd", "adamW". Default: "adamW"

    Definition Classes
    FactorizationMachinesParams → HasSolver
    Annotations
    @Since( "3.0.0" )
  99. val stepSize: DoubleParam

    Param for Step size to be used for each iteration of optimization (> 0).

    Param for Step size to be used for each iteration of optimization (> 0).

    Definition Classes
    HasStepSize
  100. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  101. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  102. final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  103. def train(dataset: Dataset[_]): FMRegressionModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    FMRegressor → Predictor
  104. 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
    Predictor → PipelineStage
  105. 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()
  106. 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
    FMRegressor → Identifiable
    Annotations
    @Since( "3.0.0" )
  107. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    PredictorParams
  108. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  109. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  110. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  111. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  112. 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 FMRegressorParams

Inherited from FactorizationMachines

Inherited from FactorizationMachinesParams

Inherited from HasWeightCol

Inherited from HasRegParam

Inherited from HasFitIntercept

Inherited from HasSeed

Inherited from HasSolver

Inherited from HasTol

Inherited from HasStepSize

Inherited from HasMaxIter

Inherited from Regressor[Vector, FMRegressor, FMRegressionModel]

Inherited from Predictor[Vector, FMRegressor, FMRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[FMRegressionModel]

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