Class/Object

org.apache.spark.ml.regression

GeneralizedLinearRegressionModel

Related Docs: object GeneralizedLinearRegressionModel | package regression

Permalink

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
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
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
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Value Members

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

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

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

    Permalink

    An alias for getOrDefault().

    An alias for getOrDefault().

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

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

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

    Permalink

    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

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

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

    Permalink

    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
    GeneralizedLinearRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "2.0.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Permalink

    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

    Permalink

    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

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

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

    Permalink

    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

    Permalink

    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

    Permalink

    Explains all params of this instance.

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

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

    Permalink

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

    Permalink

    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
  19. final val family: Param[String]

    Permalink

    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. final val featuresCol: Param[String]

    Permalink

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  21. def featuresDataType: DataType

    Permalink

    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
  22. def finalize(): Unit

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

    Permalink

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  24. final def get[T](param: Param[T]): Option[T]

    Permalink

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  25. final def getClass(): Class[_]

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

    Permalink

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  27. def getFamily: String

    Permalink

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

    Permalink

    Definition Classes
    HasFeaturesCol
  29. final def getFitIntercept: Boolean

    Permalink

    Definition Classes
    HasFitIntercept
  30. final def getLabelCol: String

    Permalink

    Definition Classes
    HasLabelCol
  31. def getLink: String

    Permalink

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

    Permalink

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

    Permalink

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

    Permalink

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

    Permalink

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  36. final def getPredictionCol: String

    Permalink

    Definition Classes
    HasPredictionCol
  37. final def getRegParam: Double

    Permalink

    Definition Classes
    HasRegParam
  38. final def getSolver: String

    Permalink

    Definition Classes
    HasSolver
  39. final def getTol: Double

    Permalink

    Definition Classes
    HasTol
  40. final def getWeightCol: String

    Permalink

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

    Permalink

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

    Permalink

    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
  43. def hasParent: Boolean

    Permalink

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  44. def hasSummary: Boolean

    Permalink

    Indicates if summary is available.

    Indicates if summary is available.

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  47. val intercept: Double

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

    Permalink

    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
  49. final def isInstanceOf[T0]: Boolean

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

    Permalink

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  51. def isTraceEnabled(): Boolean

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

    Permalink

    Param for label column name.

    Param for label column name.

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

    Permalink

    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" )
  54. final val linkPredictionCol: Param[String]

    Permalink

    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" )
  55. def log: Logger

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  57. def logDebug(msg: ⇒ String): Unit

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  59. def logError(msg: ⇒ String): Unit

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  61. def logInfo(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  62. def logName: String

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  64. def logTrace(msg: ⇒ String): Unit

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

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

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  67. final val maxIter: IntParam

    Permalink

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

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

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

    Permalink
    Definition Classes
    AnyRef
  69. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  70. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  71. val numFeatures: Int

    Permalink

    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
    GeneralizedLinearRegressionModelPredictionModel
  72. lazy val params: Array[Param[_]]

    Permalink

    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.

  73. var parent: Estimator[GeneralizedLinearRegressionModel]

    Permalink

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  74. def predict(features: Vector): Double

    Permalink

    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
  75. final val predictionCol: Param[String]

    Permalink

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  76. final val regParam: DoubleParam

    Permalink

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

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

    Permalink

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

    Permalink

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Permalink

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

    Permalink

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Permalink

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

    Permalink

    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
  83. def setFeaturesCol(value: String): GeneralizedLinearRegressionModel

    Permalink

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

    Permalink

    Sets the link prediction (linear predictor) column name.

    Sets the link prediction (linear predictor) column name.

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

    Permalink

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

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

    Permalink

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

    Permalink

    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
  88. def summary: GeneralizedLinearRegressionTrainingSummary

    Permalink

    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" )
  89. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  90. def toString(): String

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

    Permalink

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

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

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

    Permalink

    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
  93. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Permalink

    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" )
  94. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Permalink

    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()
  95. def transformImpl(dataset: Dataset[_]): DataFrame

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

    Permalink

    :: 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
    PredictionModelPipelineStage
  97. def transformSchema(schema: StructType, logging: Boolean): StructType

    Permalink

    :: 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()
  98. val uid: String

    Permalink

    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" )
  99. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Permalink

    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" )
  100. final def wait(): Unit

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

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

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

    Permalink

    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
  104. def write: MLWriter

    Permalink

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    For GeneralizedLinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

    Definition Classes
    GeneralizedLinearRegressionModelMLWritable
    Annotations
    @Since( "2.0.0" )

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