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 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

save

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 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)