LDAModel (Spark 3.5.5 JavaDoc) (original) (raw)

• 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

  All Methods Instance Methods Abstract Methods Concrete Methods

  Modifier and Type	Method and 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 Object  
     `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`  
     * ### Methods inherited from interface org.apache.spark.mllib.util.[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html "interface in org.apache.spark.mllib.util")  
     `[save](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)`

• • Method Detail

     * #### 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](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg") 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](../../../../../org/apache/spark/mllib/linalg/Matrix.html "interface in org.apache.spark.mllib.linalg") 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)