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.  def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes

    protected

    Definition Classes

    PredictorParams

16.  def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes

    protected

    Definition Classes

    PredictorParams

17.  def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes

    protected

    Definition Classes

    Predictor

18.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

19.  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

20.  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" )

21.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

22.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

23.  def fit(dataset: Dataset[_]): FMRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes

    Predictor → Estimator

24.  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" )

25.  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" )

26.  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()

27.  final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes

    HasFitIntercept

28.  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" )

29.  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

30.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

31.  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

32.  final def getFactorSize: Int
    Definition Classes

    FactorizationMachinesParams

    Annotations

    @Since( "3.0.0" )

33.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

34.  final def getFitIntercept: Boolean
    Definition Classes

    HasFitIntercept

35.  final def getFitLinear: Boolean
    Definition Classes

    FactorizationMachinesParams

    Annotations

    @Since( "3.0.0" )

36.  final def getInitStd: Double
    Definition Classes

    FactorizationMachinesParams

    Annotations

    @Since( "3.0.0" )

37.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

38.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

39.  final def getMiniBatchFraction: Double
    Definition Classes

    FactorizationMachinesParams

    Annotations

    @Since( "3.0.0" )

40.  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

41.  def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

42.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

43.  final def getRegParam: Double
    Definition Classes

    HasRegParam

44.  final def getSeed: Long
    Definition Classes

    HasSeed

45.  final def getSolver: String
    Definition Classes

    HasSolver

46.  final def getStepSize: Double
    Definition Classes

    HasStepSize

47.  final def getTol: Double
    Definition Classes

    HasTol

48.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

49.  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

50.  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

51.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

52.  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" )

53.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

    Attributes

    protected

    Definition Classes

    Logging

54.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

55.  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

56.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

57.  final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

58.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

59.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

60.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

61.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

62.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

63.  def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

64.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

65.  def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

66.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

67.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

68.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

69.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

70.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

71.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

72.  final val maxIter: IntParam

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

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

    Definition Classes

    HasMaxIter

73.  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" )

74.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

75.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

76.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

77.  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.

78.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

79.  final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes

    HasRegParam

80.  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( ... )

81.  final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes

    HasSeed

82.  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

83.  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

84.  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

85.  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

86.  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

    Definition Classes

    Params

87.  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" )

88.  def setFeaturesCol(value: String): FMRegressor
    Definition Classes

    Predictor

89.  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" )

90.  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" )

91.  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" )

92.  def setLabelCol(value: String): FMRegressor
    Definition Classes

    Predictor

93.  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" )

94.  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" )

95.  def setPredictionCol(value: String): FMRegressor
    Definition Classes

    Predictor

96.  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" )

97.  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" )

98.  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" )

99.  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" )

100.  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" )

101.  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" )

102.  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

103.  final def synchronized[T0](arg0: ⇒ T0): T0

     Definition Classes

     AnyRef

104.  def toString(): String

     Definition Classes

     Identifiable → AnyRef → Any

105.  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

106.  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

107.  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

108.  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()

109.  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" )

110.  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

111.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

112.  final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

113.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

114.  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

115.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     DefaultParamsWritable → MLWritable