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#' Calculate LDA Dimension Estimates of Tokens
#'
#' `step_lda()` creates a *specification* of a recipe step that will return the
#' lda dimension estimates of a text variable.
#'
#' @template args-recipe
#' @template args-dots
#' @template args-role_predictors
#' @template args-trained
#' @template args-columns
#' @param lda_models A WarpLDA model object from the text2vec package. If left
#' to NULL, the default, will it train its model based on the training data.
#' Look at the examples for how to fit a WarpLDA model.
#' @param num_topics integer desired number of latent topics.
#' @param prefix A prefix for generated column names, default to "lda".
#' @template args-keep_original_cols
#' @template args-skip
#' @template args-id
#'
#' @details
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble with columns `terms`
#' (the selectors or variables selected) and `num_topics` (number of topics).
#'
#' @template case-weights-not-supported
#'
#' @source \url{https://arxiv.org/abs/1301.3781}
#'
#' @template returns
#'
#' @family Steps for Numeric Variables From Tokens
#'
#' @examplesIf all(c("text2vec", "data.table") %in% rownames(installed.packages()))
#' \dontshow{library(data.table)}
#' \dontshow{data.table::setDTthreads(2)}
#' \dontshow{Sys.setenv("OMP_THREAD_LIMIT" = 2)}
#' library(recipes)
#' library(modeldata)
#' data(tate_text)
#'
#' tate_rec <- recipe(~., data = tate_text) %>%
#' step_tokenize(medium) %>%
#' step_lda(medium)
#'
#' tate_obj <- tate_rec %>%
#' prep()
#'
#' bake(tate_obj, new_data = NULL) %>%
#' slice(1:2)
#' tidy(tate_rec, number = 2)
#' tidy(tate_obj, number = 2)
#'
#' # Changing the number of topics.
#' recipe(~., data = tate_text) %>%
#' step_tokenize(medium, artist) %>%
#' step_lda(medium, artist, num_topics = 20) %>%
#' prep() %>%
#' bake(new_data = NULL) %>%
#' slice(1:2)
#'
#' # Supplying A pre-trained LDA model trained using text2vec
#' library(text2vec)
#' tokens <- word_tokenizer(tolower(tate_text$medium))
#' it <- itoken(tokens, ids = seq_along(tate_text$medium))
#' v <- create_vocabulary(it)
#' dtm <- create_dtm(it, vocab_vectorizer(v))
#' lda_model <- LDA$new(n_topics = 15)
#'
#' recipe(~., data = tate_text) %>%
#' step_tokenize(medium, artist) %>%
#' step_lda(medium, artist, lda_models = lda_model) %>%
#' prep() %>%
#' bake(new_data = NULL) %>%
#' slice(1:2)
#' @export
step_lda <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
lda_models = NULL,
num_topics = 10L,
prefix = "lda",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("lda")) {
recipes::recipes_pkg_check(required_pkgs.step_lda())
add_step(
recipe,
step_lda_new(
terms = enquos(...),
role = role,
trained = trained,
columns = columns,
lda_models = lda_models,
num_topics = num_topics,
prefix = prefix,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_lda_new <-
function(terms, role, trained, columns, lda_models, num_topics, prefix,
keep_original_cols, skip, id) {
step(
subclass = "lda",
terms = terms,
role = role,
trained = trained,
columns = columns,
lda_models = lda_models,
num_topics = num_topics,
prefix = prefix,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
#' @export
prep.step_lda <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_lda_character(training[, col_names])
check_type(training[, col_names], types = "tokenlist")
model_list <- list()
for (col_name in col_names) {
tokens <- get_tokens(training[[col_name]])
ddd <- utils::capture.output(
model_list[[col_name]] <- x$lda_models %||%
attr(word_dims(tokens, n = x$num_topics), "dict")
)
}
step_lda_new(
terms = x$terms,
role = x$role,
trained = TRUE,
columns = col_names,
lda_models = model_list,
num_topics = x$num_topics,
prefix = x$prefix,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_lda <- function(object, new_data, ...) {
col_names <- object$columns
check_new_data(col_names, object, new_data)
if (is.null(names(object$lda_models))) {
# Backwards compatibility with 1.0.3 (#230)
names(object$lda_models) <- col_names
}
for (col_name in col_names) {
tokens <- get_tokens(new_data[[col_name]])
ddd <- utils::capture.output(
tf_text <- word_dims_newtext(object$lda_models[[col_name]], tokens)
)
attr(tf_text, "dict") <- NULL
colnames(tf_text) <- paste(object$prefix, col_name, colnames(tf_text),
sep = "_"
)
tf_text <- check_name(tf_text, new_data, object, names(tf_text))
new_data <- vec_cbind(new_data, tf_text)
}
new_data <- remove_original_cols(new_data, object, col_names)
new_data
}
#' @export
print.step_lda <-
function(x, width = max(20, options()$width - 30), ...) {
title <- "Text feature extraction for "
print_step(x$columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @param x A `step_lda` object.
#' @export
tidy.step_lda <- function(x, ...) {
if (is_trained(x)) {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
num_topics = x$num_topics
)
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
num_topics = x$num_topics
)
}
res$id <- x$id
res
}
#' @rdname required_pkgs.step
#' @export
required_pkgs.step_lda <- function(x, ...) {
"textrecipes"
}
word_dims <- function(tokens, n = 10, n_iter = 20) {
it <- text2vec::itoken(tokens, ids = seq_along(tokens))
v <- text2vec::create_vocabulary(it)
v <- text2vec::prune_vocabulary(
v,
term_count_min = 2,
vocab_term_max = n * 50
)
dtm <- text2vec::create_dtm(it, text2vec::vocab_vectorizer(v))
lda_model <- text2vec::LDA$new(n_topics = n)
d <- lda_model$fit_transform(dtm, n_iter = n_iter)
d <- as.data.frame(d, stringsAsFactors = FALSE)
names(d) <- seq_len(ncol(d))
row.names(d) <- NULL
attr(d, "dict") <- lda_model
d
}
word_dims_newtext <- function(lda_model, tokens, n_iter = 20) {
it <- text2vec::itoken(tokens, ids = seq_along(tokens))
v <- text2vec::create_vocabulary(it)
v <- text2vec::prune_vocabulary(v, term_count_min = 5, doc_proportion_max = 0.2)
dtm <- text2vec::create_dtm(it, text2vec::vocab_vectorizer(v))
d <- lda_model$fit_transform(dtm, n_iter = n_iter)
d <- as.data.frame(d, stringsAsFactors = FALSE)
names(d) <- seq_len(ncol(d))
row.names(d) <- NULL
d
}
check_lda_character <- function(dat) {
character_ind <- vapply(dat, is.character, logical(1))
factor_ind <- vapply(dat, is.factor, logical(1))
all_good <- character_ind | factor_ind
if (any(all_good)) {
rlang::abort(
glue(
"All columns selected for this step should be tokenlists.",
"\n",
"See https://github.com/tidymodels/textrecipes#breaking-changes",
" for more information."
)
)
}
invisible(all_good)
}
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