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step_lda creates a specification of a recipe step that will return the lda dimension estimates of a text variable.

Usage

step_lda(
  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")
)

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 which variables are affected by the step. See recipes::selections() for more details.

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 quantities for preprocessing have been estimated.

columns

A character string of variable names that will be populated (eventually) by the terms argument. 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 "lda".

keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.

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 = FALSE.

id

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

Value

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

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and num_topics (number of topics).

Case weights

The underlying operation does not allow for case weights.

See also

Other Steps for Numeric Variables From Tokens: step_texthash(), step_tfidf(), step_tf(), step_word_embeddings()

Examples

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)
#> # A tibble: 2 × 14
#>      id artist title  year lda_m…¹ lda_m…² lda_m…³ lda_m…⁴ lda_m…⁵ lda_m…⁶
#>   <dbl> <fct>  <fct> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 21926 Absal… Prop…  1990  0.0143  0.0714   0.371  0.0857  0.0857   0.257
#> 2 20472 Auerb… Mich…  1990  0       0        0      0       0        0    
#> # … with 4 more variables: lda_medium_7 <dbl>, lda_medium_8 <dbl>,
#> #   lda_medium_9 <dbl>, lda_medium_10 <dbl>, and abbreviated variable
#> #   names ¹​lda_medium_1, ²​lda_medium_2, ³​lda_medium_3, ⁴​lda_medium_4,
#> #   ⁵​lda_medium_5, ⁶​lda_medium_6
tidy(tate_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms  num_topics id       
#>   <chr>       <int> <chr>    
#> 1 medium         10 lda_eiOnb
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 3
#>   terms  num_topics id       
#>   <chr>       <int> <chr>    
#> 1 medium         10 lda_eiOnb

# 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)
#> # A tibble: 2 × 43
#>      id title         year lda_m…¹ lda_m…² lda_m…³ lda_m…⁴ lda_m…⁵ lda_m…⁶
#>   <dbl> <fct>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 21926 Proposals f…  1990       0  0.0857       0   0.129       0  0.0286
#> 2 20472 Michael       1990       0  0            0   0           0  0     
#> # … with 34 more variables: lda_medium_7 <dbl>, lda_medium_8 <dbl>,
#> #   lda_medium_9 <dbl>, lda_medium_10 <dbl>, lda_medium_11 <dbl>,
#> #   lda_medium_12 <dbl>, lda_medium_13 <dbl>, lda_medium_14 <dbl>,
#> #   lda_medium_15 <dbl>, lda_medium_16 <dbl>, lda_medium_17 <dbl>,
#> #   lda_medium_18 <dbl>, lda_medium_19 <dbl>, lda_medium_20 <dbl>,
#> #   lda_artist_1 <dbl>, lda_artist_2 <dbl>, lda_artist_3 <dbl>,
#> #   lda_artist_4 <dbl>, lda_artist_5 <dbl>, lda_artist_6 <dbl>, …

# 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)
#> # A tibble: 2 × 33
#>      id title         year lda_m…¹ lda_m…² lda_m…³ lda_m…⁴ lda_m…⁵ lda_m…⁶
#>   <dbl> <fct>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 21926 Proposals f…  1990  0.0286  0.0571  0.0286  0.0429  0.0286   0.114
#> 2 20472 Michael       1990  0       0       0       0       0        0    
#> # … with 24 more variables: lda_medium_7 <dbl>, lda_medium_8 <dbl>,
#> #   lda_medium_9 <dbl>, lda_medium_10 <dbl>, lda_medium_11 <dbl>,
#> #   lda_medium_12 <dbl>, lda_medium_13 <dbl>, lda_medium_14 <dbl>,
#> #   lda_medium_15 <dbl>, lda_artist_1 <dbl>, lda_artist_2 <dbl>,
#> #   lda_artist_3 <dbl>, lda_artist_4 <dbl>, lda_artist_5 <dbl>,
#> #   lda_artist_6 <dbl>, lda_artist_7 <dbl>, lda_artist_8 <dbl>,
#> #   lda_artist_9 <dbl>, lda_artist_10 <dbl>, lda_artist_11 <dbl>, …