Reduce a Fitted Topic Model to Fewer Topics

Cuts the topic hierarchy (see topic_hierarchy()) at the requested count and rebuilds the model: documents keep their cluster memberships (merged, never re-clustered), centroids are recomputed from the member documents, and terms, labels, sizes, and representatives are derived afresh for the merged topics. The result is a full sbert_topic_model — every downstream verb (summary(), coherence(), predict(), representatives(), plots) works unchanged.

Usage

reduce_topics(object, n_topics, embeddings = NULL, method = "average")

Arguments

object: An sbert_topic_model returned by topics().
n_topics: Target number of topics, at least 2 and below the current count.
embeddings: The document embedding matrix used to fit object. Only needed when the model was fitted with keep_embeddings = FALSE; models fitted with keep_embeddings = TRUE carry their embeddings.
method: Agglomeration method passed to stats::hclust(). Default "average".

Value

An sbert_topic_model with n_topics topics and the same structure as a topics() fit. Names given with name_topics() are dropped, because the merged topics need new ones.

Examples

text <- c(
  "Cats chase mice", "Dogs chase balls", "Kittens nap in sunshine",
  "Stocks and bonds trade", "Markets price shares", "Banks report profit"
)
embeddings <- rbind(
  c(1, 0), c(0.95, 0.05), c(0.9, 0.1),
  c(0, 1), c(0.05, 0.95), c(0.1, 0.9)
)
topic_model <- topics(
  text, 3,
  embeddings = embeddings, n_terms = 3, keep_embeddings = TRUE
)
reduce_topics(topic_model, 2)
#> <sbert_topic_model>
#>   documents: 6 
#>   topics: 2
#>   model: precomputed embeddings
#>   algorithm: hierarchical merge of deterministic k-means topics
#>   topic sizes: 3, 3
#>   between/total SS: 99.5%