Packages

c

org.apache.spark.mllib.clustering

OnlineLDAOptimizer

final class OnlineLDAOptimizer extends LDAOptimizer with Logging

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.

Annotations
@Since("1.4.0")
Source
LDAOptimizer.scala
Linear Supertypes
Logging, LDAOptimizer, AnyRef, Any
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  1. OnlineLDAOptimizer
  2. Logging
  3. LDAOptimizer
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new OnlineLDAOptimizer()

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  9. def getKappa: Double

    Learning rate: exponential decay rate

    Learning rate: exponential decay rate

    Annotations
    @Since("1.4.0")
  10. def getMiniBatchFraction: Double

    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

    Annotations
    @Since("1.4.0")
  11. def getOptimizeDocConcentration: Boolean

    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.

    Annotations
    @Since("1.5.0")
  12. def getTau0: Double

    A (positive) learning parameter that downweights early iterations.

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

    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. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. def isTraceEnabled(): Boolean
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    protected
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    Logging
  18. def log: Logger
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    protected
    Definition Classes
    Logging
  19. def logDebug(msg: => String, throwable: Throwable): Unit
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    Definition Classes
    Logging
  20. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  21. def logDebug(entry: LogEntry): Unit
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    Logging
  22. def logDebug(msg: => String): Unit
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    protected
    Definition Classes
    Logging
  23. def logError(msg: => String, throwable: Throwable): Unit
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    protected
    Definition Classes
    Logging
  24. def logError(entry: LogEntry, throwable: Throwable): Unit
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    protected
    Definition Classes
    Logging
  25. def logError(entry: LogEntry): Unit
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    protected
    Definition Classes
    Logging
  26. def logError(msg: => String): Unit
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    protected
    Definition Classes
    Logging
  27. def logInfo(msg: => String, throwable: Throwable): Unit
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    Definition Classes
    Logging
  28. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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    Logging
  29. def logInfo(entry: LogEntry): Unit
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    Logging
  30. def logInfo(msg: => String): Unit
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    Logging
  31. def logName: String
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    Definition Classes
    Logging
  32. def logTrace(msg: => String, throwable: Throwable): Unit
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    Definition Classes
    Logging
  33. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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    protected
    Definition Classes
    Logging
  34. def logTrace(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  35. def logTrace(msg: => String): Unit
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    protected
    Definition Classes
    Logging
  36. def logWarning(msg: => String, throwable: Throwable): Unit
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    Logging
  37. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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  38. def logWarning(entry: LogEntry): Unit
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    Logging
  39. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  40. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  41. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  42. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  43. def setKappa(kappa: Double): OnlineLDAOptimizer.this.type

    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")
  44. def setMiniBatchFraction(miniBatchFraction: Double): OnlineLDAOptimizer.this.type

    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.

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

    Sets whether to optimize docConcentration parameter during training.

    Sets whether to optimize docConcentration parameter during training.

    Default: false

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

    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")
  47. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  48. def toString(): String
    Definition Classes
    AnyRef → Any
  49. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  50. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @throws(classOf[java.lang.InterruptedException]) @native()
  51. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  52. def withLogContext(context: HashMap[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 Logging

Inherited from LDAOptimizer

Inherited from AnyRef

Inherited from Any

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