Class

org.apache.spark.mllib.regression

GeneralizedLinearAlgorithm

Related Doc: package regression

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abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] extends Logging with Serializable

:: DeveloperApi :: GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM). This class should be extended with an Optimizer to create a new GLM.

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@Since( "0.8.0" ) @DeveloperApi()
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  1. GeneralizedLinearAlgorithm
  2. Serializable
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  4. Logging
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Instance Constructors

  1. new GeneralizedLinearAlgorithm()

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Abstract Value Members

  1. abstract def createModel(weights: Vector, intercept: Double): M

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    Create a model given the weights and intercept

    Create a model given the weights and intercept

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    protected
  2. abstract def optimizer: Optimizer

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    The optimizer to solve the problem.

    The optimizer to solve the problem.

    Annotations
    @Since( "0.8.0" )

Concrete Value Members

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. var addIntercept: Boolean

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    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

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    protected
  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  10. def generateInitialWeights(input: RDD[LabeledPoint]): Vector

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    Generate the initial weights when the user does not supply them

    Generate the initial weights when the user does not supply them

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    protected
  11. final def getClass(): Class[_]

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  12. def getNumFeatures: Int

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    The dimension of training features.

    The dimension of training features.

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    @Since( "1.4.0" )
  13. def hashCode(): Int

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

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  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  16. def isAddIntercept: Boolean

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    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Annotations
    @Since( "1.4.0" )
  17. final def isInstanceOf[T0]: Boolean

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  18. def isTraceEnabled(): Boolean

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    protected
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  19. def log: Logger

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

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  21. def logDebug(msg: ⇒ String): Unit

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

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  23. def logError(msg: ⇒ String): Unit

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

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  25. def logInfo(msg: ⇒ String): Unit

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  26. def logName: String

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

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  28. def logTrace(msg: ⇒ String): Unit

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

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  30. def logWarning(msg: ⇒ String): Unit

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  31. final def ne(arg0: AnyRef): Boolean

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  32. final def notify(): Unit

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  33. final def notifyAll(): Unit

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  34. var numFeatures: Int

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    The dimension of training features.

    The dimension of training features.

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    protected
  35. var numOfLinearPredictor: Int

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    In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.

    In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.

    As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.

    Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.

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    protected
  36. def run(input: RDD[LabeledPoint], initialWeights: Vector): M

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    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.

    Annotations
    @Since( "1.0.0" )
  37. def run(input: RDD[LabeledPoint]): M

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    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.

    Annotations
    @Since( "0.8.0" )
  38. def setIntercept(addIntercept: Boolean): GeneralizedLinearAlgorithm.this.type

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    Set if the algorithm should add an intercept.

    Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.

    Annotations
    @Since( "0.8.0" )
  39. def setValidateData(validateData: Boolean): GeneralizedLinearAlgorithm.this.type

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    Set if the algorithm should validate data before training.

    Set if the algorithm should validate data before training. Default true.

    Annotations
    @Since( "0.8.0" )
  40. final def synchronized[T0](arg0: ⇒ T0): T0

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  41. def toString(): String

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  42. var validateData: Boolean

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  43. val validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]

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  44. final def wait(): Unit

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

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  46. final def wait(arg0: Long): Unit

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