#' @title fit.alpha.perplexity
#' @description Calculates perplexity for values of alpha on an LDA topic model using the topicmodels package, split into training and testing sets using k-folds
#' @param dtm Document-term matrix. Constructed using the DocumentTermMatrix() command from the tm package
#' @param folds Integer. The number of folds to make training and testing sets; recommended values are '5' and '10' - note that higher values considerably increase the time that model fitting takes
#' @param alpha.values Numeric vector. Values to test alpha for. A good starting point is c(0.001, 0.01, 0.1, 1)
#' @param k Integer. Optional parameter: the value of k used in the LDA model. By default k is set to 10
#' @param beta Numeric. Optional parameter: the value of beta used in the LDA model. By default beta is set to 0.1
#' @param control.test List. Optional parameter: the LDA control list used in the LDA model. It is strongly recommended not to use this parameter unless you have good reason. Default settings are: nstart = 5, best = T, burnin = 1000, iter = 2000, thin = 500
#' @return Dataframe of perplexity for the alpha.values, calculated for the number of stipulated folds
#' @export
fit.alpha.perplexity = function(dtm, folds, alpha.values, k, beta, control.test){
if(missing(dtm)){
stop('dtm is a required parameter')
}
if(missing(folds)){
stop('folds is a required parameter')
}
if(missing(alpha.values)){
stop('alpha.values is a required parameter')
}
if(class(dtm)[[1]] != 'DocumentTermMatrix'){
stop("dtm must be of class DocumenTermMatrix. Check the class by using class()")
}
if(class(folds) != 'numeric' && class(folds) != 'integer'){
stop('folds must be of class numeric or integer. Check the class by using class()')
}
if(length(folds) != 1){
stop('folds must be a number of length 1. Check the length by using length(folds)')
}
if(class(alpha.values) != 'numeric' && class(alpha.values) != 'integer'){
stop('alpha.values must be of class numeric or integer. Check the class by using class()')
}
if(missing(k)){
k = 10
}
if(class(k) != 'numeric' && class(k) != 'integer'){
stop('k must be of class numeric or integer. Check the class by using class()')
}
if(length(k) != 1){
stop('k must be a number of length 1. Check the length by using length()')
}
if(missing(beta)){
beta = 0.1
}
if(class(beta) != 'numeric' && class(beta) != 'integer'){
stop('beta must be of class numeric or integer. Check the class by using class()')
}
if(length(beta) != 1){
stop('beta must be a number of length 1. Check the length by using length()')
}
if (missing(control.test)){
control.test = list(nstart = 5,
seed = list(1,2,3,4,5),
best = T,
burnin = 1000,
iter = 2000,
thin = 500,
alpha = 0.1,
delta = beta)}
print('fitting for alpha')
print(paste0('beta is set to: ', beta))
print(paste0('k is set to: ', k))
# set up the k-fold validation
n = nrow(dtm)
folds = folds # number of folds in the validation
set.seed(1)
splitfolds = sample(1:folds, n, replace = T)
candidate_alpha = alpha.values
# create empty alpha_perplexity dataframe
alpha_perplexity = as.data.frame(matrix(0, nrow = folds, ncol = length(candidate_alpha)))
colnames(alpha_perplexity) = candidate_alpha
# calculate perplexity for different alpha values
for (i in seq(1, length(candidate_alpha))){
alpha = candidate_alpha[i]
print(paste0('fitting topic model for alpha = ', alpha)) # to check on how much progress is being made
control.test$alpha = alpha # we just update the alpha value; that way people can still set the other params
# use 5-fold verification:
for (j in 1:folds){
train_set = dtm[splitfolds != j, ]
test_set = dtm[splitfolds == j, ]
# fit the LDA model
fitted = LDA(train_set, k = k, method = "Gibbs",
control = control.test)
# calculate perplexity for test_set
alpha_perplexity[j,i] = perplexity(fitted, newdata = test_set, control = list(seed = 1))
}}
return(alpha_perplexity)
}
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