textrecipes (version 0.0.2)

step_word2vec: Calculates word2vec dimension estimates

Description

experimental `step_word2vec` creates a *specification* of a recipe step that will return the word2vec dimension estimates of a text variable.

Usage

step_word2vec(recipe, ..., role = "predictor", trained = FALSE,
  columns = NULL, lda_models = NULL, num_topics = 10,
  prefix = "word2vec", skip = FALSE, id = rand_id("word2vec"))
# S3 method for step_word2vec
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

One or more selector functions to choose variables. For `step_word2vec`, this indicates the variables to be encoded into a list column. See [recipes::selections()] for more details. For the `tidy` method, these are not currently used.

role

For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created by the original variables will be used as predictors in a model.

trained

A logical to indicate if the recipe has been baked.

columns

A list of tibble results that define the encoding. This is `NULL` until the step is trained by [recipes::prep.recipe()].

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.

num_topics

integer desired number of latent topics.

prefix

A prefix for generated column names, default to "word2vec".

skip

A logical. Should the step be skipped when the recipe is baked by [recipes::bake.recipe()]? While all operations are baked when [recipes::prep.recipe()] is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it

x

A `step_word2vec` object.

Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any).

Examples

# NOT RUN {
library(recipes)

data(okc_text)

okc_rec <- recipe(~ ., data = okc_text) %>%
  step_word2vec(essay0)

okc_obj <- okc_rec %>%
  prep(training = okc_text, retain = TRUE)

juice(okc_obj) %>%
  slice(1:2)
tidy(okc_rec, number = 1)
tidy(okc_obj, number = 1)

# Changing the number of topics.
recipe(~ ., data = okc_text) %>%
  step_word2vec(essay0, essay1, num_topics = 20) %>%
  prep() %>%
  juice() %>%
  slice(1:2)

# Supplying A pre-trained LDA model trained using text2vec
library(text2vec)
tokens <- word_tokenizer(tolower(okc_text$essay5))
it <- itoken(tokens, ids = seq_along(okc_text$essay5))
v <- create_vocabulary(it)
dtm <- create_dtm(it, vocab_vectorizer(v))
lda_model <- LDA$new(n_topics = 15)

recipe(~ ., data = okc_text) %>%
  step_word2vec(essay0, essay1, lda_models = lda_model) %>%
  prep() %>%
  juice() %>%
  slice(1:2)

# }