org.apache.spark.mllib.regression

GeneralizedLinearAlgorithm

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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Instance Constructors

  1. new GeneralizedLinearAlgorithm()

Abstract Value Members

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

    Create a model given the weights and intercept

    Create a model given the weights and intercept

    Attributes
    protected
  2. abstract def optimizer: Optimizer

    The optimizer to solve the problem.

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

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  6. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

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  7. final def asInstanceOf[T0]: T0

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  15. def isTraceEnabled(): Boolean

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  16. def log: Logger

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

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

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

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

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

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

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  23. def logName: String

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

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

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

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

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

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. var numFeatures: Int

    The dimension of training features.

    The dimension of training features.

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    protected
  32. var numOfLinearPredictor: Int

    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.

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

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

  34. def run(input: RDD[LabeledPoint]): M

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

  35. def setIntercept(addIntercept: Boolean): GeneralizedLinearAlgorithm.this.type

    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.

  36. def setValidateData(validateData: Boolean): GeneralizedLinearAlgorithm.this.type

    Set if the algorithm should validate data before training.

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

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

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  38. def toString(): String

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  39. var validateData: Boolean

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

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  41. final def wait(): Unit

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

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