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[_]): GeneralizedLinearRegression.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[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

8.  def copy(extra: ParamMap): GeneralizedLinearRegression

   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

   GeneralizedLinearRegression → Predictor → Estimator → PipelineStage → Params

   Annotations

   @Since( "2.0.0" )

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 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

16.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

17.  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

18.  final val family: Param[String]

    Param for the name of family which is a description of the error distribution to be used in the model.

    Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".

    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.0.0" )

19.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

20.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

21.  def fit(dataset: Dataset[_]): GeneralizedLinearRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes

    Predictor → Estimator

22.  def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GeneralizedLinearRegressionModel]

    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" )

23.  def fit(dataset: Dataset[_], paramMap: ParamMap): GeneralizedLinearRegressionModel

    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" )

24.  def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GeneralizedLinearRegressionModel

    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()

25.  final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes

    HasFitIntercept

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.  def getFamily: String
    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.0.0" )

30.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

31.  final def getFitIntercept: Boolean
    Definition Classes

    HasFitIntercept

32.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

33.  def getLink: String
    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.0.0" )

34.  def getLinkPower: Double
    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.2.0" )

35.  def getLinkPredictionCol: String
    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.0.0" )

36.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

37.  def getOffsetCol: String
    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.3.0" )

38.  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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

40.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

41.  final def getRegParam: Double
    Definition Classes

    HasRegParam

42.  final def getSolver: String
    Definition Classes

    HasSolver

43.  final def getTol: Double
    Definition Classes

    HasTol

44.  def getVariancePower: Double
    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.2.0" )

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.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

51.  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

52.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

54.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

55.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

56.  final val link: Param[String]

    Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.

    Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.

    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.0.0" )

57.  final val linkPower: DoubleParam

    Param for the index in the power link function.

    Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.

    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.2.0" )

58.  final val linkPredictionCol: Param[String]

    Param for link prediction (linear predictor) column name.

    Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.

    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.0.0" )

59.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

66.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

71.  final val maxIter: IntParam

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

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

    Definition Classes

    HasMaxIter

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

    Definition Classes

    AnyRef

73.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

74.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

75.  final val offsetCol: Param[String]

    Param for offset column name.

    Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.

    Definition Classes

    GeneralizedLinearRegressionBase

    Annotations

    @Since( "2.3.0" )

76.  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.

77.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

78.  final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes

    HasRegParam

79.  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( ... )

80.  final def set(paramPair: ParamPair[_]): GeneralizedLinearRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

81.  final def set(param: String, value: Any): GeneralizedLinearRegression.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

82.  final def set[T](param: Param[T], value: T): GeneralizedLinearRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

83.  final def setDefault(paramPairs: ParamPair[_]*): GeneralizedLinearRegression.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

84.  final def setDefault[T](param: Param[T], value: T): GeneralizedLinearRegression.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

85.  def setFamily(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param family.

    Sets the value of param family. Default is "gaussian".

    Annotations

    @Since( "2.0.0" )

86.  def setFeaturesCol(value: String): GeneralizedLinearRegression
    Definition Classes

    Predictor

87.  def setFitIntercept(value: Boolean): GeneralizedLinearRegression.this.type

    Sets if we should fit the intercept.

    Sets if we should fit the intercept. Default is true.

    Annotations

    @Since( "2.0.0" )

88.  def setLabelCol(value: String): GeneralizedLinearRegression
    Definition Classes

    Predictor

89.  def setLink(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param link.

    Sets the value of param link. Used only when family is not "tweedie".

    Annotations

    @Since( "2.0.0" )

90.  def setLinkPower(value: Double): GeneralizedLinearRegression.this.type

    Sets the value of param linkPower.

    Sets the value of param linkPower. Used only when family is "tweedie".

    Annotations

    @Since( "2.2.0" )

91.  def setLinkPredictionCol(value: String): GeneralizedLinearRegression.this.type

    Sets the link prediction (linear predictor) column name.

    Sets the link prediction (linear predictor) column name.

    Annotations

    @Since( "2.0.0" )

92.  def setMaxIter(value: Int): GeneralizedLinearRegression.this.type

    Sets the maximum number of iterations (applicable for solver "irls").

    Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.

    Annotations

    @Since( "2.0.0" )

93.  def setOffsetCol(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param offsetCol.

    Sets the value of param offsetCol. If this is not set or empty, we treat all instance offsets as 0.0. Default is not set, so all instances have offset 0.0.

    Annotations

    @Since( "2.3.0" )

94.  def setPredictionCol(value: String): GeneralizedLinearRegression
    Definition Classes

    Predictor

95.  def setRegParam(value: Double): GeneralizedLinearRegression.this.type

    Sets the regularization parameter for L2 regularization.

    Sets the regularization parameter for L2 regularization. The regularization term is

    $$ 0.5 * regParam * L2norm(coefficients)^2 $$

    Default is 0.0.

    Annotations

    @Since( "2.0.0" )

96.  def setSolver(value: String): GeneralizedLinearRegression.this.type

    Sets the solver algorithm used for optimization.

    Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.

    Annotations

    @Since( "2.0.0" )

97.  def setTol(value: Double): GeneralizedLinearRegression.this.type

    Sets the convergence tolerance of iterations.

    Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

    Annotations

    @Since( "2.0.0" )

98.  def setVariancePower(value: Double): GeneralizedLinearRegression.this.type

    Sets the value of param variancePower.

    Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.

    Annotations

    @Since( "2.2.0" )

99.  def setWeightCol(value: String): GeneralizedLinearRegression.this.type

    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.

    Annotations

    @Since( "2.0.0" )

100.  final val solver: Param[String]

     The solver algorithm for optimization.

     The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"

     Definition Classes

     GeneralizedLinearRegressionBase → HasSolver

     Annotations

     @Since( "2.0.0" )

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

     Definition Classes

     AnyRef

102.  def toString(): String

     Definition Classes

     Identifiable → AnyRef → Any

103.  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

104.  def train(dataset: Dataset[_]): GeneralizedLinearRegressionModel

     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

     GeneralizedLinearRegression → Predictor

105.  def transformSchema(schema: StructType): StructType

     :: DeveloperApi ::

     :: DeveloperApi ::

     Check transform validity and derive the output schema from the input schema.

     We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

     Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

     Definition Classes

     Predictor → PipelineStage

106.  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()

107.  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

     GeneralizedLinearRegression → Identifiable

     Annotations

     @Since( "2.0.0" )

108.  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

     Definition Classes

     GeneralizedLinearRegressionBase → PredictorParams

     Annotations

     @Since( "2.0.0" )

109.  final val variancePower: DoubleParam

     Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.

     Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.

     Definition Classes

     GeneralizedLinearRegressionBase

     Annotations

     @Since( "2.2.0" )

110.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @native() @throws( ... )

113.  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

114.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     DefaultParamsWritable → MLWritable