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c

org.apache.spark.mllib.regression

GeneralizedLinearAlgorithm

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

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

Annotations
@Since("0.8.0")
Source
GeneralizedLinearAlgorithm.scala
Linear Supertypes
Serializable, Logging, AnyRef, Any
Known Subclasses
LogisticRegressionWithLBFGS, LogisticRegressionWithSGD, SVMWithSGD, LassoWithSGD, LinearRegressionWithSGD, RidgeRegressionWithSGD
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Inherited
  1. GeneralizedLinearAlgorithm
  2. Serializable
  3. Logging
  4. AnyRef
  5. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GeneralizedLinearAlgorithm()

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

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.

    The optimizer to solve the problem.

    Annotations
    @Since("0.8.0")

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def MDC(key: LogKey, value: Any): MDC
    Attributes
    protected
    Definition Classes
    Logging
  5. var addIntercept: Boolean

    Whether to add intercept (default: false).

    Whether to add intercept (default: false).

    Attributes
    protected
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  10. def generateInitialWeights(input: RDD[LabeledPoint]): Vector

    Generate the initial weights when the user does not supply them

    Generate the initial weights when the user does not supply them

    Attributes
    protected
  11. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  12. def getNumFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Annotations
    @Since("1.4.0")
  13. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  14. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  16. def isAddIntercept: Boolean

    Get if the algorithm uses addIntercept

    Get if the algorithm uses addIntercept

    Annotations
    @Since("1.4.0")
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  19. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  20. def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
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    protected
    Definition Classes
    Logging
  21. def logDebug(msg: => String, throwable: Throwable): Unit
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    protected
    Definition Classes
    Logging
  22. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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    protected
    Definition Classes
    Logging
  23. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logError(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logError(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  30. def logInfo(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  31. def logInfo(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  34. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  35. def logTrace(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  36. def logTrace(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  39. def logWarning(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. def logWarning(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  41. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  42. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  43. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  44. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  45. var numFeatures: Int

    The dimension of training features.

    The dimension of training features.

    Attributes
    protected
  46. 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
  47. 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.

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

    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")
  49. 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.

    Annotations
    @Since("0.8.0")
  50. 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.

    Annotations
    @Since("0.8.0")
  51. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  52. def toString(): String
    Definition Classes
    AnyRef → Any
  53. var validateData: Boolean
    Attributes
    protected
  54. val validators: Seq[(RDD[LabeledPoint]) => Boolean]
    Attributes
    protected
  55. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  56. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  57. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  58. def withLogContext(context: Map[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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