R/LDA.R

Defines functions LDA_set logLik.LDA_VEM check_LDA_set_inputs prep_LDA_control select_LDA package_LDA_set LDA_msg LDA_set_control

Documented in check_LDA_set_inputs LDA_msg LDA_set LDA_set_control logLik.LDA_VEM package_LDA_set prep_LDA_control select_LDA

#' @title Run a set of Latent Dirichlet Allocation models
#' 
#' @description For a given dataset consisting of counts of words across 
#'   multiple documents in a corpus, conduct multiple Latent Dirichlet 
#'   Allocation (LDA) models (using the Variational Expectation 
#'   Maximization (VEM) algorithm; Blei \emph{et al.} 2003) to account for [1]  
#'   uncertainty in the number of latent topics and [2] the impact of initial
#'   values in the estimation procedure. \cr \cr
#'   \code{LDA_set} is a list wrapper of \code{\link[topicmodels]{LDA}}
#'   in the \code{topicmodels} package (Grun and Hornik 2011). \cr \cr
#'   \code{check_LDA_set_inputs} checks that all of the inputs 
#'   are proper for \code{LDA_set} (that the table of observations is 
#'   conformable to a matrix of integers, the number of topics is an integer, 
#'   the number of seeds is an integer and the controls list is proper).
#'   
#' @param document_term_table Table of observation count data (rows: 
#'   documents, columns: terms. May be a class \code{matrix} or 
#'   \code{data.frame} but must be conformable to a matrix of integers,
#'   as verified by \code{\link{check_document_term_table}}.   
#'  
#' @param topics Vector of the number of topics to evaluate for each model.
#'   Must be conformable to \code{integer} values.
#'
#' @param nseeds Number of seeds (replicate starts) to use for each 
#'   value of \code{topics}. Must be conformable to \code{integer} value.
#'
#' @param control A \code{list} of parameters to control the running and 
#'   selecting of LDA models. Values not input assume default values set 
#'   by \code{\link{LDA_set_control}}. Values for running the LDAs replace 
#'   defaults in (\code{LDAcontol}, see \code{\link[topicmodels]{LDA}} (but if
#'    \code{seed} is given, it will be overwritten; use \code{iseed} instead).
#' 
#' @return 
#'   \code{LDA_set}: \code{list} (class: \code{LDA_set}) of LDA models 
#'   (class: \code{LDA_VEM}).
#'   \code{check_LDA_set_inputs}: an error message is thrown if any input is 
#'   improper, otherwise \code{NULL}.
#' 
#' @references 
#'   Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet
#'   Allocation. \emph{Journal of Machine Learning Research} 
#'   \strong{3}:993-1022.
#'   \href{http://jmlr.csail.mit.edu/papers/v3/blei03a.html}{link}.
#'
#'   Grun B. and K. Hornik. 2011. topicmodels: An R Package for Fitting Topic
#'   Models. \emph{Journal of Statistical Software} \strong{40}:13.
#'   \href{https://www.jstatsoft.org/article/view/v040i13}{link}.
#'
#' @examples 
#'   data(rodents)
#'   lda_data <- rodents$document_term_table
#'   r_LDA <- LDA_set(lda_data, topics = 2, nseeds = 2)                         
#' 
#' @export
#'
LDA_set <- function(document_term_table, topics = 2, nseeds = 1, 
                    control = list()){
  check_LDA_set_inputs(document_term_table, topics, nseeds, control)
  control <- do.call("LDA_set_control", control)
  mod_topics <- rep(topics, each = length(seq(2, nseeds * 2, 2)))
  iseed <- control$iseed
  mod_seeds <- rep(seq(iseed, iseed + (nseeds - 1)* 2, 2), length(topics))
  nmods <- length(mod_topics)
  mods <- vector("list", length = nmods)
  for (i in 1:nmods){
    LDA_msg(mod_topics[i], mod_seeds[i], control)
    control_i <- prep_LDA_control(seed = mod_seeds[i], control = control)
    mods[[i]] <- LDA(document_term_table, k = mod_topics[i], 
                     control = control_i)
  }
  package_LDA_set(mods, mod_topics, mod_seeds)
}

#' @title Calculate the log likelihood of a VEM LDA model fit
#'
#' @description Imported but updated calculations from topicmodels package, as
#'   applied to Latent Dirichlet Allocation fit with Variational Expectation 
#'   Maximization via \code{\link[topicmodels]{LDA}}. 
#'
#' @details The number of degrees of freedom is 1 (for alpha) plus the number
#'   of entries in the document-topic matrix. The number of observations is 
#'   the number of entries in the document-term matrix.
#'
#' @param object A \code{LDA_VEM}-class object.
#'
#' @param ... Not used, simply included to maintain method compatibility.
#'
#' @return Log likelihood of the model \code{logLik}, also with \code{df}
#'   (degrees of freedom) and \code{nobs} (number of observations) values.
#'
#' @references 
#'   Buntine, W. 2002. Variational extensions to EM and multinomial PCA. 
#'   \emph{European Conference on Machine Learning, Lecture Notes in Computer 
#'   Science} \strong{2430}:23-34. \href{https://bit.ly/327sltH}{link}.
#'
#'   Grun B. and K. Hornik. 2011. topicmodels: An R Package for Fitting Topic
#'   Models. \emph{Journal of Statistical Software} \strong{40}:13.
#'   \href{https://www.jstatsoft.org/article/view/v040i13}{link}.
#'
#'   Hoffman, M. D., D. M. Blei, and F. Bach. 2010. Online learning for 
#'   latent Dirichlet allocation. \emph{Advances in Neural Information 
#'   Processing Systems} \strong{23}:856-864.
#'   \href{https://bit.ly/2LEr5sb}{link}.
#'
#' @examples 
#'   data(rodents)
#'   lda_data <- rodents$document_term_table
#'   r_LDA <- LDA_set(lda_data, topics = 2)   
#'   logLik(r_LDA[[1]])
#'
#' @export
#'
logLik.LDA_VEM <- function(object, ...){
  val <- sum(object@loglikelihood)
  df <- as.integer(object@control@estimate.alpha) + length(object@beta)
  attr(val, "df") <- df
  attr(val, "nobs") <- object@Dim[1] * object@Dim[2]
  class(val) <- "logLik"
  val
}

#' @rdname LDA_set
#'   
#' @export
#'
check_LDA_set_inputs <- function(document_term_table, topics, nseeds, 
                                 control){
  check_document_term_table(document_term_table)
  check_topics(topics)
  check_seeds(nseeds)
  check_control(control)
}

#' @title Set the control inputs to include the seed
#' 
#' @description Update the control list for the LDA model with the specific
#'   seed as indicated. And remove controls not used within the LDA itself.
#'   
#' @param seed \code{integer} used to set the seed of the specific model. 
#'
#' @param control Named list of control parameters to be used in 
#'   \code{\link[topicmodels]{LDA}} Note that if \code{control} has an 
#'   element named \code{seed} it will be overwritten by the \code{seed} 
#'   argument of \code{prep_LDA_control}.
#'
#' @return \code{list} of controls to be used in the LDA. 
#'
#' @examples
#'   prep_LDA_control(seed = 1) 
#'
#' @export
#'
prep_LDA_control <- function(seed, control = list()){
  control$quiet <- NULL
  control$measurer <- NULL
  control$selector <- NULL
  control$iseed <- NULL
  control$seed <- seed
  control
}

#' @title Select the best LDA model(s) for use in time series
#'
#' @description Select the best model(s) of interest from an
#'   \code{LDA_set} object, based on a set of user-provided functions. The
#'   functions default to choosing the model with the lowest AIC value.
#'
#' @param LDA_models An object of class \code{LDA_set} produced by
#'   \code{\link{LDA_set}}.
#'
#' @param control A \code{list} of parameters to control the running and 
#'   selecting of LDA models. Values not input assume default values set 
#'   by \code{\link{LDA_set_control}}. Values for running the LDAs replace 
#'   defaults in (\code{LDAcontol}, see \code{\link[topicmodels]{LDA}} (but if
#'    \code{seed} is given, it will be overwritten; use \code{iseed} instead).
#'
#' @return A reduced version of \code{LDA_models} that only includes the 
#'   selected LDA model(s). The returned object is still an object of
#'   class \code{LDA_set}.
#'
#' @examples
#'   data(rodents)
#'   lda_data <- rodents$document_term_table
#'   r_LDA <- LDA_set(lda_data, topics = 2, nseeds = 2)  
#'   select_LDA(r_LDA)                       
#'
#' @export
#'
select_LDA <- function(LDA_models = NULL, control = list()){
  if("LDA_set" %in% attr(LDA_models, "class") == FALSE){
    stop("LDA_models must be of class LDA_set")
  }
  control <- do.call("LDA_set_control", control)
  measurer <- control$measurer
  selector <- control$selector  
  lda_measured <- vapply(LDA_models, measurer, 0) %>%
                  matrix(ncol = 1)
  lda_selected <- apply(lda_measured, 2, selector) 
  which_selected <- which(lda_measured %in% lda_selected)
  out <- LDA_models[which_selected]
  class(out)  <- c("LDA_set", "list") 
  out
}

#' @title Package the output from LDA_set
#'
#' @description Name the elements (LDA models) and set the class 
#'   (\code{LDA_set}) of the models returned by \code{\link{LDA_set}}.
#'
#' @param mods Fitted models returned from \code{\link[topicmodels]{LDA}}.
#'
#' @param mod_topics Vector of \code{integer} values corresponding to the 
#'   number of topics in each model.
#' 
#' @param mod_seeds Vector of \code{integer} values corresponding to the 
#'   seed used for each model.
#'
#' @return \code{lis} (class: \code{LDA_set}) of LDA models (class: 
#'   \code{LDA_VEM}).
#'
#' @examples 
#' \donttest{
#'   data(rodents)
#'   document_term_table <- rodents$document_term_table
#'   topics <- 2
#'   nseeds <- 2
#'   control <- LDA_set_control()
#'   mod_topics <- rep(topics, each = length(seq(2, nseeds * 2, 2)))
#'   iseed <- control$iseed
#'   mod_seeds <- rep(seq(iseed, iseed + (nseeds - 1)* 2, 2), length(topics))
#'   nmods <- length(mod_topics)
#'   mods <- vector("list", length = nmods)
#'   for (i in 1:nmods){
#'     LDA_msg(mod_topics[i], mod_seeds[i], control)
#'     control_i <- prep_LDA_control(seed = mod_seeds[i], control = control)
#'     mods[[i]] <- topicmodels::LDA(document_term_table, k = mod_topics[i], 
#'                      control = control_i)
#'   }
#'   package_LDA_set(mods, mod_topics, mod_seeds)
#' }
#' 
#' @export
#'
package_LDA_set <- function(mods, mod_topics, mod_seeds){
  if (!("LDA_VEM" %in% class(mods[[1]]))){
    stop("mods not of class LDA_VEM")
  }
  check_topics(mod_topics)
  if (!is.numeric(mod_seeds) || any(mod_seeds%% 1 != 0)){
    stop("mod_seeds must be integers")
  }
  names(mods) <- paste0("k: ", mod_topics, ", seed: ", mod_seeds)
  class(mods) <- c("LDA_set", "list")  
  mods
}

#' @title Create the model-running-message for an LDA
#'
#' @description Produce and print the message for a given LDA model.
#'
#' @param mod_topics \code{integer} value corresponding to the number of 
#'   topics in the model.
#' 
#' @param mod_seeds \code{integer} value corresponding to the seed used for 
#'   the model.
#'
#' @param control Class \code{LDA_controls} list of control parameters to be
#'   used in \code{LDA} (note that "seed" will be overwritten).
#'
#' @examples
#'   LDA_msg(mod_topics = 4, mod_seeds = 2)
#'
#' @export
#'
LDA_msg <- function(mod_topics, mod_seeds, control = list()){
  control <- do.call("LDA_set_control", control)
  check_topics(mod_topics)
  check_seeds(mod_seeds)
  topic_msg <- paste0("Running LDA with ", mod_topics, " topics ")
  seed_msg <- paste0("(seed ", mod_seeds, ")")
  messageq(paste0(topic_msg, seed_msg), control$quiet)
}

#' @title Create control list for set of LDA models
#'
#' @description This function provides a simple creation and definition of 
#'   the list used to control the set of LDA models. It is set up to be easy
#'   to work with the existing control capacity of 
#'   \code{\link[topicmodels]{LDA}}.
#'
#' @param quiet \code{logical} indicator of whether the model should run 
#'   quietly.
#'
#' @param measurer,selector Function names for use in evaluation of the LDA
#'   models. \code{measurer} is used to create a value for each model
#'   and \code{selector} operates on the values to choose the model(s) to 
#'   pass on. 
#'
#' @param iseed \code{integer} initial seed for the model set. 
#'
#' @param ... Additional arguments to be passed to 
#'   \code{\link[topicmodels]{LDA}} as a \code{control} input.
#'
#' @return \code{list} for controlling the LDA model fit.
#'
#' @examples
#'   LDA_set_control()
#'
#' @export
#'
LDA_set_control <- function(quiet = FALSE, measurer = AIC, selector = min,
                            iseed = 2, ...){
  list(quiet = quiet, measurer = measurer, selector = selector, 
       iseed = iseed, ...)
}

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LDATS documentation built on March 20, 2020, 1:09 a.m.