org.apache.spark.mllib.clustering

LDA

class LDA extends Logging

:: Experimental ::

Latent Dirichlet Allocation (LDA), a topic model designed for text documents.

Terminology:

Currently, the underlying implementation uses Expectation-Maximization (EM), implemented according to the Asuncion et al. (2009) paper referenced below.

References:

Annotations
@Experimental()
See also

Latent Dirichlet allocation (Wikipedia)

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Instance Constructors

  1. new LDA()

Value Members

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  11. def getAlpha: Double

    Alias for getDocConcentration

  12. def getBeta: Double

    Alias for getTopicConcentration

  13. def getCheckpointInterval: Int

    Period (in iterations) between checkpoints.

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

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  15. def getDocConcentration: Double

    Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").

    Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").

    This is the parameter to a symmetric Dirichlet distribution.

  16. def getK: Int

    Number of topics to infer.

    Number of topics to infer. I.e., the number of soft cluster centers.

  17. def getMaxIterations: Int

    Maximum number of iterations for learning.

  18. def getSeed: Long

    Random seed

  19. def getTopicConcentration: Double

    Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.

    Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.

    This is the parameter to a symmetric Dirichlet distribution.

    Note: The topics' distributions over terms are called "beta" in the original LDA paper by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.

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  22. def isTraceEnabled(): Boolean

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  23. def log: Logger

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  30. def logName: String

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  36. final def notify(): Unit

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  37. final def notifyAll(): Unit

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  38. def run(documents: JavaPairRDD[Long, Vector]): DistributedLDAModel

    Java-friendly version of run()

  39. def run(documents: RDD[(Long, Vector)]): DistributedLDAModel

    Learn an LDA model using the given dataset.

    Learn an LDA model using the given dataset.

    documents

    RDD of documents, which are term (word) count vectors paired with IDs. The term count vectors are "bags of words" with a fixed-size vocabulary (where the vocabulary size is the length of the vector). Document IDs must be unique and >= 0.

    returns

    Inferred LDA model

  40. def setAlpha(alpha: Double): LDA.this.type

    Alias for setDocConcentration()

  41. def setBeta(beta: Double): LDA.this.type

    Alias for setTopicConcentration()

  42. def setCheckpointInterval(checkpointInterval: Int): LDA.this.type

    Period (in iterations) between checkpoints (default = 10).

    Period (in iterations) between checkpoints (default = 10). Checkpointing helps with recovery (when nodes fail). It also helps with eliminating temporary shuffle files on disk, which can be important when LDA is run for many iterations. If the checkpoint directory is not set in org.apache.spark.SparkContext, this setting is ignored.

    See also

    org.apache.spark.SparkContext#setCheckpointDir

  43. def setDocConcentration(docConcentration: Double): LDA.this.type

    Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").

    Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").

    This is the parameter to a symmetric Dirichlet distribution.

    This value should be > 1.0, where larger values mean more smoothing (more regularization). If set to -1, then docConcentration is set automatically. (default = -1 = automatic)

    Automatic setting of parameter:

    • For EM: default = (50 / k) + 1.
      • The 50/k is common in LDA libraries.
      • The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM.

    Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), but values in (0,1) are not yet supported.

  44. def setK(k: Int): LDA.this.type

    Number of topics to infer.

    Number of topics to infer. I.e., the number of soft cluster centers. (default = 10)

  45. def setMaxIterations(maxIterations: Int): LDA.this.type

    Maximum number of iterations for learning.

    Maximum number of iterations for learning. (default = 20)

  46. def setSeed(seed: Long): LDA.this.type

    Random seed

  47. def setTopicConcentration(topicConcentration: Double): LDA.this.type

    Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.

    Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.

    This is the parameter to a symmetric Dirichlet distribution.

    Note: The topics' distributions over terms are called "beta" in the original LDA paper by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.

    This value should be > 0.0. If set to -1, then topicConcentration is set automatically. (default = -1 = automatic)

    Automatic setting of parameter:

    • For EM: default = 0.1 + 1.
      • The 0.1 gives a small amount of smoothing.
      • The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM.

    Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), but values in (0,1) are not yet supported.

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