Class/Object

org.apache.spark.ml.regression

GeneralizedLinearRegressionModel

Related Docs: object GeneralizedLinearRegressionModel | package regression

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class GeneralizedLinearRegressionModel extends RegressionModel[Vector, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with MLWritable

:: Experimental :: Model produced by GeneralizedLinearRegression.

Annotations
@Experimental() @Since( "2.0.0" )
Source
GeneralizedLinearRegression.scala
Linear Supertypes
MLWritable, GeneralizedLinearRegressionBase, HasSolver, HasWeightCol, HasRegParam, HasTol, HasMaxIter, HasFitIntercept, RegressionModel[Vector, GeneralizedLinearRegressionModel], PredictionModel[Vector, GeneralizedLinearRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[GeneralizedLinearRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GeneralizedLinearRegressionModel
  2. MLWritable
  3. GeneralizedLinearRegressionBase
  4. HasSolver
  5. HasWeightCol
  6. HasRegParam
  7. HasTol
  8. HasMaxIter
  9. HasFitIntercept
  10. RegressionModel
  11. PredictionModel
  12. PredictorParams
  13. HasPredictionCol
  14. HasFeaturesCol
  15. HasLabelCol
  16. Model
  17. Transformer
  18. PipelineStage
  19. Logging
  20. Params
  21. Serializable
  22. Serializable
  23. Identifiable
  24. AnyRef
  25. Any
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Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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

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    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

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. val coefficients: Vector

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    Annotations
    @Since( "2.0.0" )
  9. def copy(extra: ParamMap): GeneralizedLinearRegressionModel

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    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.

    Definition Classes
    GeneralizedLinearRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "2.0.0" )
    See also

    defaultCopy()

  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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
  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    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
  12. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  14. def evaluate(dataset: Dataset[_]): GeneralizedLinearRegressionSummary

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    Evaluate the model on the given dataset, returning a summary of the results.

    Evaluate the model on the given dataset, returning a summary of the results.

    Annotations
    @Since( "2.0.0" )
  15. def explainParam(param: Param[_]): String

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    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
  16. def explainParams(): String

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    Explains all params of this instance.

    Explains all params of this instance.

    Definition Classes
    Params
    See also

    explainParam()

  17. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  18. final def extractParamMap(extra: ParamMap): ParamMap

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    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 < user-supplied values < 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 < user-supplied values < extra.

    Definition Classes
    Params
  19. final val family: Param[String]

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    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" and "gamma". Default is "gaussian".

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  20. lazy val familyAndLink: FamilyAndLink

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  21. lazy val familyObj: Family

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  22. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def featuresDataType: DataType

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    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes
    protected
    Definition Classes
    PredictionModel
  24. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. final val fitIntercept: BooleanParam

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    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]

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    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[_]

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    Definition Classes
    AnyRef → Any
  28. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  29. def getFamily: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  30. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  31. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  32. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  33. def getLink: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  34. def getLinkPredictionCol: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  35. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  36. final def getOrDefault[T](param: Param[T]): T

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

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  38. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  39. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  40. final def getSolver: String

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    Definition Classes
    HasSolver
  41. final def getTol: Double

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    Definition Classes
    HasTol
  42. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  43. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  44. def hasParam(paramName: String): Boolean

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    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
  45. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  46. def hasSummary: Boolean

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    Indicates if summary is available.

    Indicates if summary is available.

    Annotations
    @Since( "2.0.0" )
  47. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  48. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  49. val intercept: Double

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    Annotations
    @Since( "2.0.0" )
  50. final def isDefined(param: Param[_]): Boolean

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    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
  51. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  52. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  53. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  54. final val labelCol: Param[String]

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  55. final val link: Param[String]

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    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".

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  56. lazy val linkObj: Link

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  57. final val linkPredictionCol: Param[String]

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    Param for link prediction (linear predictor) column name.

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

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  58. def log: Logger

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    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  65. def logName: String

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    protected
    Definition Classes
    Logging
  66. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  68. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  70. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  71. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  72. final def notify(): Unit

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    Definition Classes
    AnyRef
  73. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  74. def numFeatures: Int

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    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  75. lazy val params: Array[Param[_]]

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    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.

    Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

    Definition Classes
    Params
  76. var parent: Estimator[GeneralizedLinearRegressionModel]

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    The parent estimator that produced this model.

    The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.

    Definition Classes
    Model
  77. def predict(features: Vector): Double

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    Predict label for the given features.

    Predict label for the given features. This internal method is used to implement transform() and output predictionCol.

    Attributes
    protected
    Definition Classes
    GeneralizedLinearRegressionModelPredictionModel
  78. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  79. final val regParam: DoubleParam

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    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  80. def save(path: String): Unit

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    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 def set(paramPair: ParamPair[_]): GeneralizedLinearRegressionModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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    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
  83. final def set[T](param: Param[T], value: T): GeneralizedLinearRegressionModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  84. final def setDefault(paramPairs: ParamPair[_]*): GeneralizedLinearRegressionModel.this.type

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    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
  85. final def setDefault[T](param: Param[T], value: T): GeneralizedLinearRegressionModel.this.type

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    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
  86. def setFeaturesCol(value: String): GeneralizedLinearRegressionModel

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    Definition Classes
    PredictionModel
  87. def setLinkPredictionCol(value: String): GeneralizedLinearRegressionModel.this.type

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    Sets the link prediction (linear predictor) column name.

    Sets the link prediction (linear predictor) column name.

    Annotations
    @Since( "2.0.0" )
  88. def setParent(parent: Estimator[GeneralizedLinearRegressionModel]): GeneralizedLinearRegressionModel

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  89. def setPredictionCol(value: String): GeneralizedLinearRegressionModel

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    Definition Classes
    PredictionModel
  90. final val solver: Param[String]

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    Param for the solver algorithm for optimization.

    Param for the solver algorithm for optimization. If this is not set or empty, default value is 'auto'.

    Definition Classes
    HasSolver
  91. def summary: GeneralizedLinearRegressionTrainingSummary

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    Gets R-like summary of model on training set.

    Gets R-like summary of model on training set. An exception is thrown if there is no summary available.

    Annotations
    @Since( "2.0.0" )
  92. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  93. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  94. final val tol: DoubleParam

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    Param for the convergence tolerance for iterative algorithms.

    Param for the convergence tolerance for iterative algorithms.

    Definition Classes
    HasTol
  95. def transform(dataset: Dataset[_]): DataFrame

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    Transforms dataset by reading from featuresCol, calling predict(), and storing the predictions as a new column predictionCol.

    Transforms dataset by reading from featuresCol, calling predict(), and storing the predictions as a new column predictionCol.

    dataset

    input dataset

    returns

    transformed dataset with predictionCol of type Double

    Definition Classes
    GeneralizedLinearRegressionModelPredictionModelTransformer
  96. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  97. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  98. def transformImpl(dataset: Dataset[_]): DataFrame

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    Attributes
    protected
    Definition Classes
    GeneralizedLinearRegressionModelPredictionModel
  99. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictionModelPipelineStage
  100. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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()
  101. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    GeneralizedLinearRegressionModelIdentifiable
    Annotations
    @Since( "2.0.0" )
  102. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    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., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Definition Classes
    GeneralizedLinearRegressionBase → PredictorParams
    Annotations
    @Since( "2.0.0" )
  103. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  104. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  105. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  106. final val weightCol: Param[String]

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    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
  107. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    GeneralizedLinearRegressionModelMLWritable
    Annotations
    @Since( "2.0.0" )

Deprecated Value Members

  1. def validateParams(): Unit

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    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) Will be removed in 2.1.0. Checks should be merged into transformSchema.

Inherited from MLWritable

Inherited from GeneralizedLinearRegressionBase

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasRegParam

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasFitIntercept

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Transformer

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