Class

org.apache.spark.mllib.clustering

OnlineLDAOptimizer

Related Doc: package clustering

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final class OnlineLDAOptimizer extends LDAOptimizer with Logging

:: DeveloperApi ::

An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic distribution adaptively.

Original Online LDA paper: Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.

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@Since( "1.4.0" ) @DeveloperApi()
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Logging, LDAOptimizer, AnyRef, Any
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Instance Constructors

  1. new OnlineLDAOptimizer()

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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. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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  9. final def getClass(): Class[_]

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  10. def getKappa: Double

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    Learning rate: exponential decay rate

    Learning rate: exponential decay rate

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    @Since( "1.4.0" )
  11. def getMiniBatchFraction: Double

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    Mini-batch fraction, which sets the fraction of document sampled and used in each iteration

    Mini-batch fraction, which sets the fraction of document sampled and used in each iteration

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    @Since( "1.4.0" )
  12. def getOptimizeDocConcentration: Boolean

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    Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training.

    Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for document-topic distribution) will be optimized during training.

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    @Since( "1.5.0" )
  13. def getTau0: Double

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    A (positive) learning parameter that downweights early iterations.

    A (positive) learning parameter that downweights early iterations. Larger values make early iterations count less.

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

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

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  16. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  17. final def isInstanceOf[T0]: Boolean

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  18. def isTraceEnabled(): Boolean

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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. def setKappa(kappa: Double): OnlineLDAOptimizer.this.type

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    Learning rate: exponential decay rate---should be between (0.5, 1.0] to guarantee asymptotic convergence.

    Learning rate: exponential decay rate---should be between (0.5, 1.0] to guarantee asymptotic convergence. Default: 0.51, based on the original Online LDA paper.

    Annotations
    @Since( "1.4.0" )
  35. def setMiniBatchFraction(miniBatchFraction: Double): OnlineLDAOptimizer.this.type

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    Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in each iteration.

    Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in each iteration.

    Annotations
    @Since( "1.4.0" )
    Note

    This should be adjusted in synch with LDA.setMaxIterations() so the entire corpus is used. Specifically, set both so that maxIterations * miniBatchFraction is at least 1. Default: 0.05, i.e., 5% of total documents.

  36. def setOptimizeDocConcentration(optimizeDocConcentration: Boolean): OnlineLDAOptimizer.this.type

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    Sets whether to optimize docConcentration parameter during training.

    Sets whether to optimize docConcentration parameter during training.

    Default: false

    Annotations
    @Since( "1.5.0" )
  37. def setTau0(tau0: Double): OnlineLDAOptimizer.this.type

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    A (positive) learning parameter that downweights early iterations.

    A (positive) learning parameter that downweights early iterations. Larger values make early iterations count less. Default: 1024, following the original Online LDA paper.

    Annotations
    @Since( "1.4.0" )
  38. final def synchronized[T0](arg0: ⇒ T0): T0

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  39. def toString(): String

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  40. final def wait(): Unit

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

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

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Inherited from Logging

Inherited from LDAOptimizer

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