perplexity
Methods for Function perplexity
Determine the perplexity of a fitted model.
- Keywords
- methods
Usage
perplexity(object, newdata, ...)
"perplexity"(object, newdata, control, ...)
"perplexity"(object, newdata, control, use_theta = TRUE,
estimate_theta = TRUE, ...)
"perplexity"(object, newdata, control, use_theta = TRUE,
estimate_theta = TRUE, ...)Arguments
- object
- Object of class
"TopicModel"or"Gibbs_list". - newdata
- If missing, the perplexity for the data to which the
model was fitted is determined. For objects fitted using Gibbs sampling
newdataneeds to be specified. - control
- If missing, the
controlof the fitted model is used with suitable changes of the relevant parameters (see Details). - use_theta
- Object of class
"logical". IfTRUEthe estimated topic distributions for the documents are used. Otherwise equal weights are assigned to the topics for each document. - estimate_theta
- Object of class
"logical". IfFALSEthe 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 innewdatais used for weighting the term-document occurrences. - ...
- Further arguments passed to the different methods.
Details
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.
Value
-
A numeric value.
References
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.