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abstract class LDAModel extends Saveable

Latent Dirichlet Allocation (LDA) model.

This abstraction permits for different underlying representations, including local and distributed data structures.

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@Since( "1.3.0" )
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LDAModel.scala
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  1. abstract def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])]

    Return the topics described by weighted terms.

    Return the topics described by weighted terms.

    maxTermsPerTopic

    Maximum number of terms to collect for each topic.

    returns

    Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.

    Annotations
    @Since( "1.3.0" )
  2. abstract def docConcentration: Vector

    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 Dirichlet distribution.

    Annotations
    @Since( "1.5.0" )
  3. abstract def gammaShape: Double

    Shape parameter for random initialization of variational parameter gamma.

    Shape parameter for random initialization of variational parameter gamma. Used for variational inference for perplexity and other test-time computations.

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  4. abstract def k: Int

    Number of topics

    Number of topics

    Annotations
    @Since( "1.3.0" )
  5. abstract def save(sc: SparkContext, path: String): Unit

    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

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    Saveable
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    @Since( "1.3.0" )
  6. abstract def topicConcentration: 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.

    Annotations
    @Since( "1.5.0" )
    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.

  7. abstract def topicsMatrix: Matrix

    Inferred topics, where each topic is represented by a distribution over terms.

    Inferred topics, where each topic is represented by a distribution over terms. This is a matrix of size vocabSize x k, where each column is a topic. No guarantees are given about the ordering of the topics.

    Annotations
    @Since( "1.3.0" )
  8. abstract def vocabSize: Int

    Vocabulary size (number of terms or terms in the vocabulary)

    Vocabulary size (number of terms or terms in the vocabulary)

    Annotations
    @Since( "1.3.0" )

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  6. def describeTopics(): Array[(Array[Int], Array[Double])]

    Return the topics described by weighted terms.

    Return the topics described by weighted terms.

    WARNING: If vocabSize and k are large, this can return a large object!

    returns

    Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.

    Annotations
    @Since( "1.3.0" )
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