lda_cvb: An implementation of LDA that uses collapsed variational bayes for inference. More...

#include <lda_cvb.h>

Inheritance diagram for meta::topics::lda_cvb:

Public Member Functions
	lda_cvb (std::shared_ptr< index::forward_index > idx, uint64_t num_topics, double alpha, double beta)
	Constructs the lda model over the given documents, with the given number of topics, and hyperparameters \(\alpha\) and \(\beta\) for the priors on \(\phi\) (topic distributions) and \(\theta\) (topic proportions), respectively. More...

virtual	~lda_cvb ()=default
	Destructor: virtual for potential subclassing.

void	run (uint64_t num_iters, double convergence=1e-3)
	Runs the variational inference algorithm for a maximum number of iterations, or until the given convergence criterion is met. More...

Public Member Functions inherited from meta::topics::lda_model
	lda_model (std::shared_ptr< index::forward_index > idx, uint64_t num_topics)
	Constructs an lda_model over the given set of documents and with a fixed number of topics. More...

virtual	~lda_model ()=default
	Destructor. More...

void	save_doc_topic_distributions (const std::string &filename) const
	Saves the topic proportions \(\theta_d\) for each document to the given file. More...

void	save_topic_term_distributions (const std::string &filename) const
	Saves the term distributions \(\phi_j\) for each topic to the given file. More...

void	save (const std::string &prefix) const
	Saves the current model to a set of files beginning with prefix: prefix.phi, prefix.theta, and prefix.terms. More...

Protected Member Functions
void	initialize ()
	Initializes the parameters randomly.

double	perform_iteration (uint64_t iter)
	Performs one iteration of the inference algorithm. More...

virtual double	compute_term_topic_probability (term_id term, topic_id topic) const override

virtual double	compute_doc_topic_probability (doc_id doc, topic_id topic) const override

Protected Member Functions inherited from meta::topics::lda_model
lda_model &	operator= (const lda_model &)=delete
	lda_models cannot be copy assigned.

	lda_model (const lda_model &)=delete
	lda_models cannot be copy constructed.

Protected Attributes
std::vector< std::vector< stats::multinomial< topic_id > > >	gamma_
	Variational distributions \(\gamma_{ij}\), which represent the soft topic assignments for each word occurrence \(i\) in document \(j\). More...

std::vector< stats::multinomial< term_id > >	phi_
	The word distributions for each topic, \(\phi_t\).

std::vector< stats::multinomial< topic_id > >	theta_
	The topic distributions for each document, \(\theta_d\).

Protected Attributes inherited from meta::topics::lda_model
std::shared_ptr< index::forward_index >	idx_
	The index containing the documents for the model.

size_t	num_topics_
	The number of topics.

size_t	num_words_
	The number of total unique words.

Detailed Description

lda_cvb: An implementation of LDA that uses collapsed variational bayes for inference.

Specifically, it uses the CVB0 algorithm detailed in Asuncion et. al.

See also: http://www.ics.uci.edu/~asuncion/pubs/UAI_09.pdf

Constructor & Destructor Documentation

meta::topics::lda_cvb::lda_cvb	(	std::shared_ptr< index::forward_index >	idx,
		uint64_t	num_topics,
		double	alpha,
		double	beta
	)

Constructs the lda model over the given documents, with the given number of topics, and hyperparameters \(\alpha\) and \(\beta\) for the priors on \(\phi\) (topic distributions) and \(\theta\) (topic proportions), respectively.

Parameters

idx	The index containing the documents to model
num_topics	The number of topics to infer
alpha	The hyperparameter for the Dirichlet prior over \(\phi\)
beta	The hyperparameter for the Dirichlet prior over \(\theta\)

Member Function Documentation

void meta::topics::lda_cvb::run	(	uint64_t	num_iters,
		double	convergence = `1e-3`
	)

virtual

Runs the variational inference algorithm for a maximum number of iterations, or until the given convergence criterion is met.

The convergence criterion is determined as the maximum difference in any of the variational parameters \(\gamma_{dij}\) in a given iteration.

Parameters

num_iters	The maximum number of iterations to run the sampler for
convergence	The lowest maximum difference in any \(\gamma_{dij}\) to be allowed before considering the inference to have converged

Implements meta::topics::lda_model.

double meta::topics::lda_cvb::perform_iteration ( uint64_t iter )

protected

Performs one iteration of the inference algorithm.

Parameters

iter	The current iteration number

Returns: the maximum change in any of the \(\gamma_{dij}\)s

double meta::topics::lda_cvb::compute_term_topic_probability	(	term_id	term,
		topic_id	topic
	)		const

overrideprotectedvirtual

Returns: the probability that the given term appears in the given topic

Parameters

term	The term we are concerned with
topic	The topic we are concerned with

Implements meta::topics::lda_model.

double meta::topics::lda_cvb::compute_doc_topic_probability	(	doc_id	doc,
		topic_id	topic
	)		const

overrideprotectedvirtual

Returns: the probability that the given topic is picked for the given document

Parameters

doc	The document we are concerned with
topic	The topic we are concerned with

Implements meta::topics::lda_model.

Member Data Documentation

std::vector<std::vector<stats::multinomial<topic_id> > > meta::topics::lda_cvb::gamma_

protected

Variational distributions \(\gamma_{ij}\), which represent the soft topic assignments for each word occurrence \(i\) in document \(j\).

Indexed as gamma_[d][i]

The documentation for this class was generated from the following files:

/home/chase/projects/meta/include/topics/lda_cvb.h
/home/chase/projects/meta/src/topics/lda_cvb.cpp

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation