Determine the perplexity of a fitted model.
perplexity(object, newdata, …)# S4 method for VEM,simple_triplet_matrix
perplexity(object, newdata, control, …)
# S4 method for Gibbs,simple_triplet_matrix
perplexity(object, newdata, control, use_theta = TRUE,
estimate_theta = TRUE, …)
# S4 method for Gibbs_list,simple_triplet_matrix
perplexity(object, newdata, control, use_theta = TRUE,
estimate_theta = TRUE, …)
Object of class "TopicModel" or "Gibbs_list".
If missing, the perplexity for the data to which the
model was fitted is determined. For objects fitted using Gibbs sampling
newdata needs to be specified.
If missing, the control of the fitted model is
used with suitable changes of the relevant parameters (see
Details).
Object of class "logical". If TRUE
the estimated topic distributions for the documents are
used. Otherwise equal weights are assigned to the topics for each document.
Object of class "logical". If FALSE the
data provided is assumed to be the same as the data used for fitting the
model. The topic distributions therefore do not need to be estimated
and the data in newdata is used for weighting the
term-document occurrences.
Further arguments passed to the different methods.
A numeric value.
The specified control is modified to ensure that (1)
estimate.beta=FALSE and (2) nstart=1.
For "Gibbs_list" objects the control is further modified
to have (1) iter=thin and (2) best=TRUE and the model is
fitted to the new data with this control for each available
iteration. The perplexity is then determined by averaging over the
same number of iterations.
If a list is supplied as object, it is assumed that it
consists of several models which were fitted using different starting
configurations.
Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993--1022.
Griffiths T.L., Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences of the United States of America, 101, Suppl. 1, 5228--5235.
Newman D., Asuncion A., Smyth P., Welling M. (2009). Distributed Algorithms for Topic Models. Journal of Machine Learning Research, 10, 1801--1828.