step_texthash() creates a specification of a recipe step that will
convert a token variable into multiple numeric variables
using the hashing trick.
Usage
step_texthash(
recipe,
...,
role = "predictor",
trained = FALSE,
columns = NULL,
signed = TRUE,
num_terms = 1024L,
prefix = "texthash",
sparse = "auto",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("texthash")
)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 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
termsargument. This isNULLuntil the step is trained byrecipes::prep.recipe().- signed
A logical, indicating whether to use a signed hash-function to reduce collisions when hashing. Defaults to TRUE.
- num_terms
An integer, the number of variables to output. Defaults to 1024.
- 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". Ifsparse = "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.recipe()? While all operations are baked whenrecipes::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 usingskip = 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).
Details
Feature hashing, or the hashing trick, is a transformation of a text variable into a new set of numerical variables. This is done by applying a hashing function over the tokens and using the hash values as feature indices. This allows for a low memory representation of the text. This implementation is done using the MurmurHash3 method.
The argument num_terms controls the number of indices that the hashing
function will map to. This is the tuning parameter for this transformation.
Since the hashing function can map two different tokens to the same index,
will a higher value of num_terms result in a lower chance of collision.
The new components will have names that begin with prefix, then
the name of the variable, followed by the tokens all separated by
-. The variable names are padded with zeros. For example if
prefix = "hash", and if num_terms < 10, their names will be
hash1 - hash9. If num_terms = 101, their names will be
hash001 - hash101.
Tidying
When you tidy() this step, a tibble is returned with
columns terms, value and id:
- terms
character, the selectors or variables selected
- value
logical, is it signed?
- length
integer, number of terms
- id
character, id of this step
Tuning Parameters
This step has 2 tuning parameters:
signed: Signed Hash Value (type: logical, default: TRUE)num_terms: # Hash Features (type: integer, default: 1024)
Sparse data
This step produces sparse columns if sparse = "yes" is being set. The
default value "auto" won't trigger production fo 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 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".
See also
step_tokenize() to turn characters into tokens
step_text_normalization() to perform text normalization.
Other Steps for Numeric Variables From Tokens:
step_lda(),
step_tf(),
step_tfidf(),
step_word_embeddings()
Examples
library(recipes)
library(modeldata)
data(tate_text)
tate_rec <- recipe(~., data = tate_text) %>%
step_tokenize(medium) %>%
step_tokenfilter(medium, max_tokens = 10) %>%
step_texthash(medium)
tate_obj <- tate_rec %>%
prep()
bake(tate_obj, tate_text)
#> # A tibble: 4,284 × 1,028
#> id artist title year texthash_medium_0001 texthash_medium_0002
#> <dbl> <fct> <fct> <dbl> <int> <int>
#> 1 21926 Absalon Prop… 1990 0 0
#> 2 20472 Auerba… Mich… 1990 0 0
#> 3 20474 Auerba… Geof… 1990 0 0
#> 4 20473 Auerba… Jake 1990 0 0
#> 5 20513 Auerba… To t… 1990 0 0
#> 6 21389 Ayres,… Phaë… 1990 0 0
#> 7 121187 Barlow… Unti… 1990 0 0
#> 8 19455 Baseli… Gree… 1990 0 0
#> 9 20938 Beatti… Pres… 1990 0 0
#> 10 105941 Beuys,… Jose… 1990 0 0
#> # ℹ 4,274 more rows
#> # ℹ 1,022 more variables: texthash_medium_0003 <int>,
#> # texthash_medium_0004 <int>, texthash_medium_0005 <int>,
#> # texthash_medium_0006 <int>, texthash_medium_0007 <int>,
#> # texthash_medium_0008 <int>, texthash_medium_0009 <int>,
#> # texthash_medium_0010 <int>, texthash_medium_0011 <int>,
#> # texthash_medium_0012 <int>, texthash_medium_0013 <int>, …
tidy(tate_rec, number = 3)
#> # A tibble: 1 × 4
#> terms value length id
#> <chr> <lgl> <int> <chr>
#> 1 medium NA NA texthash_BcqvO
tidy(tate_obj, number = 3)
#> # A tibble: 1 × 4
#> terms value length id
#> <chr> <lgl> <int> <chr>
#> 1 medium TRUE 1024 texthash_BcqvO