sparse = "yes" doesn't take effect when
weight_scheme = "double normalization" as it doesn't produce sparse data.
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")
)An updated version of recipe with the new step added
to the sequence of existing steps (if any).
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.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
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().
A character determining the weighting scheme for the term frequency calculations. Must be one of "binary", "raw count", "term frequency", "log normalization" or "double normalization". Defaults to "raw count".
A numeric weight used if weight_scheme is set to "double
normalization". Defaults to 0.5.
A character vector of strings to be considered.
The words that will be used to calculate the term frequency will
be stored here once this preprocessing step has be trained by
recipes::prep.recipe().
A character string that will be the prefix to the resulting new variables. See notes below.
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".
A logical to keep the original variables in the
output. Defaults to FALSE.
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.
A character string that is unique to this step to identify it.
When you tidy() this step, a tibble is returned with
columns terms, value, and id:
character, the selectors or variables selected
character, the weighting scheme
character, id of this step
This step has 2 tuning parameters:
weight_scheme: Term Frequency Weight Method (type: character, default: raw count)
weight: Weight (type: double, default: 0.5)
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".
The underlying operation does not allow for case weights.
step_tf() creates a specification of a recipe step that will convert a
token variable into multiple variables containing the token
counts.
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 words 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 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.
step_tokenize() to turn characters into tokens
Other Steps for Numeric Variables From Tokens:
step_lda(),
step_texthash(),
step_tfidf(),
step_word_embeddings()
# \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)
tidy(tate_rec, number = 2)
tidy(tate_obj, number = 2)
# }
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