An LDA topic model implemented using the Approximate Distributed LDA algorithm. More...

#include <parallel_lda_gibbs.h>

Inheritance diagram for meta::topics::parallel_lda_gibbs:

Public Member Functions
virtual	~parallel_lda_gibbs ()=default
	Destructor: virtual for potential subclassing.

Public Member Functions inherited from meta::topics::lda_gibbs
	lda_gibbs (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_gibbs ()=default
	Destructor: virtual for potential subclassing.

virtual void	run (uint64_t num_iters, double convergence=1e-6)
	Runs the sampler 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
virtual void	initialize () override
	Initializes the first set of topic assignments for inference. More...

virtual void	perform_iteration (uint64_t iter, bool init=false) override
	Performs a sampling iteration of the AD-LDA algorithm. More...

virtual void	decrease_counts (topic_id topic, term_id term, doc_id doc) override
	Decreases all counts associated with the given topic, term, and document by one. More...

virtual void	increase_counts (topic_id topic, term_id term, doc_id doc) override
	Increases all counts associated with the given topic, term, and document by one. More...

virtual double	compute_sampling_weight (term_id term, doc_id doc, topic_id topic) const override
	Computes a weight proportional to \(P(z_i = j \| w, \boldsymbol{z})\). More...

Protected Member Functions inherited from meta::topics::lda_gibbs
topic_id	sample_topic (term_id term, doc_id doc)
	Samples a topic from the full conditional distribution \(P(z_i = j \| w, \boldsymbol{z})\). 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

double	corpus_log_likelihood () const

lda_gibbs &	operator= (const lda_gibbs &)=delete
	lda_gibbs cannot be copy assigned.

	lda_gibbs (const lda_gibbs &other)=delete
	lda_gibbs cannot be copy constructed.

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
parallel::thread_pool	pool_
	The thread pool used for parallelization.

std::unordered_map< std::thread::id, std::vector< stats::multinomial< term_id > > >	phi_diffs_
	Stores the difference in topic_term counts on a per-thread basis for use in the reduction step. More...

Protected Attributes inherited from meta::topics::lda_gibbs
std::vector< std::vector< topic_id > >	doc_word_topic_
	The topic assignment for every word in every document. 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, \(\theta_d\).

std::mt19937_64	rng_
	The random number generator for the sampler.

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

An LDA topic model implemented using the Approximate Distributed LDA algorithm.

Based on the algorithm detailed by David Newman et. al.

See also: http://www.jmlr.org/papers/volume10/newman09a/newman09a.pdf

Member Function Documentation

void meta::topics::parallel_lda_gibbs::initialize ( )

overrideprotectedvirtual

Initializes the first set of topic assignments for inference.

Employs an online application of the sampler where counts are only considered for the words observed so far through the loop.

Reimplemented from meta::topics::lda_gibbs.

void meta::topics::parallel_lda_gibbs::perform_iteration	(	uint64_t	iter,
		bool	init = `false`
	)

overrideprotectedvirtual

Performs a sampling iteration of the AD-LDA algorithm.

This consists of splitting up the sampling of (document, word) topic assignments across threads, keeping for each thread a difference in counts for the potentially shared topic counts. Once the sampling has finished, the counts are reduced down (serially) before the iteration is completed.

Parameters

iter	The current iteration number
init	Whether or not this iteration should use the online method for initializing the sampler

Reimplemented from meta::topics::lda_gibbs.

void meta::topics::parallel_lda_gibbs::decrease_counts	(	topic_id	topic,
		term_id	term,
		doc_id	doc
	)

overrideprotectedvirtual

Decreases all counts associated with the given topic, term, and document by one.

Parameters

topic	The topic in question
term	The term in question
doc	The document in question

Reimplemented from meta::topics::lda_gibbs.

void meta::topics::parallel_lda_gibbs::increase_counts	(	topic_id	topic,
		term_id	term,
		doc_id	doc
	)

overrideprotectedvirtual

Increases all counts associated with the given topic, term, and document by one.

Parameters

topic	The topic in question
term	The term in question
doc	The document in question

Reimplemented from meta::topics::lda_gibbs.

double meta::topics::parallel_lda_gibbs::compute_sampling_weight	(	term_id	term,
		doc_id	doc,
		topic_id	topic
	)		const

overrideprotectedvirtual

Computes a weight proportional to \(P(z_i = j | w, \boldsymbol{z})\).

Parameters

term	The current word we are sampling for
doc	The document in which the term resides
topic	The topic \(j\) we want to compute the probability for

Returns: a weight proportional to the probability that the given term in the given document belongs to the given topic

Reimplemented from meta::topics::lda_gibbs.

Member Data Documentation

std::unordered_map<std::thread::id, std::vector<stats::multinomial<term_id> > > meta::topics::parallel_lda_gibbs::phi_diffs_

protected

Stores the difference in topic_term counts on a per-thread basis for use in the reduction step.

Indexed as [thread_id][topic]

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

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

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

Member Data Documentation