tidylda (version 0.0.7)

refit.tidylda: Update a Latent Dirichlet Allocation topic model

Description

Update an LDA model using collapsed Gibbs sampling.

Usage

# S3 method for tidylda
refit(
  object,
  new_data,
  iterations = NULL,
  burnin = -1,
  prior_weight = 1,
  additional_k = 0,
  additional_eta_sum = 250,
  optimize_alpha = FALSE,
  calc_likelihood = FALSE,
  calc_r2 = FALSE,
  return_data = FALSE,
  threads = 1,
  verbose = TRUE,
  ...
)

Value

Returns an S3 object of class c("tidylda").

Arguments

object

a fitted object of class tidylda.

new_data

A document term matrix or term co-occurrence matrix of class dgCMatrix.

iterations

Integer number of iterations for the Gibbs sampler to run.

burnin

Integer number of burnin iterations. If burnin is greater than -1, the resulting "beta" and "theta" matrices are an average over all iterations greater than burnin.

prior_weight

Numeric, 0 or greater or NA. The weight of the beta as a prior from the base model. See Details, below.

additional_k

Integer number of topics to add, defaults to 0.

additional_eta_sum

Numeric magnitude of prior for additional topics. Ignored if additional_k is 0. Defaults to 250.

optimize_alpha

Logical. Experimental. Do you want to optimize alpha every iteration? Defaults to FALSE.

calc_likelihood

Logical. Do you want to calculate the log likelihood every iteration? Useful for assessing convergence. Defaults to FALSE.

calc_r2

Logical. Do you want to calculate R-squared after the model is trained? Defaults to FALSE.

return_data

Logical. Do you want new_data returned as part of the model object?

threads

Number of parallel threads, defaults to 1.

verbose

Logical. Do you want to print a progress bar out to the console? Defaults to TRUE.

...

Additional arguments, currently unused

Details

refit allows you to (a) update the probabilities (i.e. weights) of a previously-fit model with new data or additional iterations and (b) optionally use beta of a previously-fit LDA topic model as the eta prior for the new model. This is tuned by setting beta_as_prior = FALSE or beta_as_prior = TRUE respectively.

prior_weight tunes how strong the base model is represented in the prior. If prior_weight = 1, then the tokens from the base model's training data have the same relative weight as tokens in new_data. In other words, it is like just adding training data. If prior_weight is less than 1, then tokens in new_data are given more weight. If prior_weight is greater than 1, then the tokens from the base model's training data are given more weight.

If prior_weight is NA, then the new eta is equal to eta from the old model, with new tokens folded in. (For handling of new tokens, see below.) Effectively, this just controls how the sampler initializes (described below), but does not give prior weight to the base model.

Instead of initializing token-topic assignments in the manner for new models (see tidylda), the update initializes in 2 steps:

First, topic-document probabilities (i.e. theta) are obtained by a call to predict.tidylda using method = "dot" for the documents in new_data. Next, both beta and theta are passed to an internal function, initialize_topic_counts, which assigns topics to tokens in a manner approximately proportional to the posteriors and executes a single Gibbs iteration.

refit handles the addition of new vocabulary by adding a flat prior over new tokens. Specifically, each entry in the new prior is equal to the 10th percentile of eta from the old model. The resulting model will have the total vocabulary of the old model plus any new vocabulary tokens. In other words, after running refit.tidylda ncol(beta) >= ncol(new_data) where beta is from the new model and new_data is the additional data.

You can add additional topics by setting the additional_k parameter to an integer greater than zero. New entries to alpha have a flat prior equal to the median value of alpha in the old model. (Note that if alpha itself is a flat prior, i.e. scalar, then the new topics have the same value for their prior.) New entries to eta have a shape from the average of all previous topics in eta and scaled by additional_eta_sum.

Examples

# \donttest{
# load a document term matrix
data(nih_sample_dtm)

d1 <- nih_sample_dtm[1:50, ]

d2 <- nih_sample_dtm[51:100, ]

# fit a model
m <- tidylda(d1,
  k = 10,
  iterations = 200, burnin = 175
)

# update an existing model by adding documents using old model as prior
m2 <- refit(
  object = m,
  new_data = rbind(d1, d2),
  iterations = 200,
  burnin = 175,
  prior_weight = 1
)

# use an old model to initialize new model and not use old model as prior
m3 <- refit(
  object = m,
  new_data = d2, # new documents only
  iterations = 200,
  burnin = 175,
  prior_weight = NA
)

# add topics while updating a model by adding documents
m4 <- refit(
  object = m,
  new_data = rbind(d1, d2),
  additional_k = 3,
  iterations = 200,
  burnin = 175
)
# }