Package org.apache.spark.mllib.clustering

Class LDAModel

Object
org.apache.spark.mllib.clustering.LDAModel
All Implemented Interfaces:
Saveable
Direct Known Subclasses:
DistributedLDAModel, LocalLDAModel
public abstract class LDAModel extends Object implements Saveable
Latent Dirichlet Allocation (LDA) model.

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

  • Method Summary

    Modifier and Type
    Method
    Description
    scala.Tuple2<int[],double[]>[]
    describeTopics()
    Return the topics described by weighted terms.
    abstract scala.Tuple2<int[],double[]>[]
    describeTopics(int maxTermsPerTopic)
    Return the topics described by weighted terms.
    abstract Vector
    docConcentration()
    Concentration parameter (commonly named "alpha") for the prior placed on documents' distributions over topics ("theta").
    abstract int
    k()
    Number of topics
    abstract double
    topicConcentration()
    Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' distributions over terms.
    abstract Matrix
    topicsMatrix()
    Inferred topics, where each topic is represented by a distribution over terms.
    abstract int
    vocabSize()
    Vocabulary size (number of terms or terms in the vocabulary)

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface org.apache.spark.mllib.util.Saveable

    save

  • Method Details

    • describeTopics

      public abstract scala.Tuple2<int[],double[]>[] describeTopics(int maxTermsPerTopic)
      Return the topics described by weighted terms.

      Parameters:
      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.

    • describeTopics

      public scala.Tuple2<int[],double[]>[] describeTopics()
      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.

    • docConcentration

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

      This is the parameter to a Dirichlet distribution.

      Returns:
      (undocumented)

    • k

      public abstract int k()
      Number of topics

    • topicConcentration

      public abstract double topicConcentration()
      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.

      Returns:
      (undocumented)
      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.

    • topicsMatrix

      public abstract Matrix topicsMatrix()
      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.
      Returns:
      (undocumented)

    • vocabSize

      public abstract int vocabSize()
      Vocabulary size (number of terms or terms in the vocabulary)