topics (version 0.21.0)

topicsPreds: Predict topic distributions

Description

The function to predict the topics of a new document with the trained model.

Usage

topicsPreds(
  model,
  data,
  num_iterations = 100,
  seed = 42,
  save_dir,
  load_dir = NULL
)

Value

A tibble of the predictions

Arguments

model

(list) The trained model

data

(tibble) The new data

num_iterations

(integer) The number of iterations to run the model

seed

(integer) The seed to set for reproducibility

save_dir

(string) The directory to save the model, if NULL, the predictions will not be saved

load_dir

(string) The directory to load the model from, if NULL, the predictions will not be loaded

Examples

# \donttest{
# Predict topics for new data with the trained model
save_dir_temp <- tempfile()

dtm <- topicsDtm(
data = dep_wor_data$Depphrase, 
save_dir = save_dir_temp)

model <- topicsModel(dtm = dtm, # output of topicsDtm()
                     num_topics = 20,
                     num_top_words = 10,
                     num_iterations = 1000,
                     seed = 42,
                     save_dir = save_dir_temp)
                     
preds <- topicsPreds(
model = model, # output of topicsModel()
data = dep_wor_data$Depphrase, 
save_dir = save_dir_temp)
# }