Term frequency of Tokens

step_tf() creates a specification of a recipe step that will convert a token variable into multiple variables containing the token counts.

Usage

step_tf(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  weight_scheme = "raw count",
  weight = 0.5,
  vocabulary = NULL,
  res = NULL,
  prefix = "tf",
  sparse = "auto",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("tf")
)

Arguments

recipe: A recipes::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 new columns created by this step from 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 vector of the selected variable names. This is NULL until the step is trained by recipes::prep.recipe(), and should not be set by hand.
weight_scheme: A single string determining the weighting scheme for the term frequency calculations. Must be one of "binary", "raw count", "term frequency", "log normalization", or "double normalization". See details for what each scheme does. Note that sparse = "yes" has no effect with "double normalization", as it doesn't produce sparse data. Defaults to "raw count".
weight: A single numeric weight used if weight_scheme is set to "double normalization". Defaults to 0.5.
vocabulary: A character vector of the tokens to create variables for. If NULL, the default, all tokens found in the training data are used.
res: A named list of the tokens used for each selected variable. This is NULL until the step is trained by recipes::prep.recipe() and should not be set by hand.
prefix: A character string that will be the prefix to the resulting new variables. See notes below.
sparse: A single string. Should the columns produced be sparse vectors? Can take the values "yes", "no", and "auto". If sparse = "auto" then workflows can determine the best option. Defaults to "auto".
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()? While all operations are baked when recipes::prep() 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.

Value

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

Details

It is strongly advised to use step_tokenfilter before using step_tf to limit the number of variables created, otherwise you might run into memory issues. A good strategy is to start with a low token count and go up according to how much RAM you want to use.

Term frequency is a weight of how many times each token appears in each observation. There are different ways to calculate the weight and this step can do it in a couple of ways. Setting the argument weight_scheme to "binary" will result in a set of binary variables denoting if a token is present in the observation. "raw count" will count the times a token is present in the observation. "term frequency" will divide the count by the total number of in-vocabulary tokens in the document to limit the effect of the document length as longer documents tends to have the word present more times but not necessarily at a higher percentage. "log normalization" takes the log of 1 plus the count, adding 1 is done to avoid taking log of 0. Finally "double normalization" is the raw frequency divided by the raw frequency of the most occurring term in the document. This is then multiplied by weight and weight is added to the result. This is again done to prevent a bias towards longer documents.

The new columns will have names that begin with prefix, then the name of the variable, then the token, all separated by _. For example, with the default prefix = "tf", a variable named text, and the token "word", the new column will be named tf_text_word.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, and id:

terms: character, the selectors or variables selected
value: character, the weighting scheme
id: character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

weight_scheme: Term Frequency Weight Method (type: character, default: raw count)
weight: Weight (type: double, default: 0.5)

Sparse data

This step produces sparse columns if sparse = "yes" is being set. The default value "auto" won't trigger production of sparse columns if a recipe is recipes::prep()ed, but allows for a workflow to toggle to "yes" or "no" depending on whether the model supports recipes::sparse_data and if the model is expected to run faster with the data.

The mechanism for determining how much sparsity is produced isn't perfect, and there will be times when you want to manually overwrite by setting sparse = "yes" or sparse = "no".

Case weights

The underlying operation does not allow for case weights.

Examples

# \donttest{
library(recipes)
library(modeldata)
data(tate_text)

tate_rec <- recipe(~., data = tate_text) |>
  step_tokenize(medium) |>
  step_tf(medium)

tate_obj <- tate_rec |>
  prep()

bake(tate_obj, tate_text)
#> # A tibble: 4,284 × 956
#>        id artist     title  year tf_medium_1 tf_medium_10 tf_medium_100
#>     <dbl> <fct>      <fct> <dbl>       <int>        <int>         <int>
#>  1  21926 Absalon    Prop…  1990           0            0             0
#>  2  20472 Auerbach,… Mich…  1990           0            0             0
#>  3  20474 Auerbach,… Geof…  1990           0            0             0
#>  4  20473 Auerbach,… Jake   1990           0            0             0
#>  5  20513 Auerbach,… To t…  1990           0            0             0
#>  6  21389 Ayres, OB… Phaë…  1990           0            0             0
#>  7 121187 Barlow, P… Unti…  1990           0            0             0
#>  8  19455 Baselitz,… Gree…  1990           0            0             0
#>  9  20938 Beattie, … Pres…  1990           0            0             0
#> 10 105941 Beuys, Jo… Jose…  1990           0            0             0
#> # ℹ 4,274 more rows
#> # ℹ 949 more variables: tf_medium_11 <int>, tf_medium_12 <int>,
#> #   tf_medium_13 <int>, tf_medium_133 <int>, tf_medium_14 <int>,
#> #   tf_medium_15 <int>, tf_medium_151 <int>, tf_medium_16 <int>,
#> #   tf_medium_160 <int>, tf_medium_16mm <int>, tf_medium_18 <int>,
#> #   tf_medium_19 <int>, tf_medium_2 <int>, tf_medium_20 <int>,
#> #   tf_medium_2000 <int>, tf_medium_201 <int>, tf_medium_21 <int>, …

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms  value id      
#>   <chr>  <chr> <chr>   
#> 1 medium NA    tf_wjcKo
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 3
#>   terms  value     id      
#>   <chr>  <chr>     <chr>   
#> 1 medium raw count tf_wjcKo
# }