step_feature_hash {embed}

Dummy Variables Creation via Feature Hashing

Description

step_feature_hash() is being deprecated in favor of textrecipes::step_dummy_hash(). This function creates a specification of a recipe step that will convert nominal data (e.g. character or factors) into one or more numeric binary columns using the levels of the original data.

Usage

step_feature_hash(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_hash = 2^6,
  preserve = deprecated(),
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("feature_hash")
)

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 this step. See 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.

num_hash

The number of resulting dummy variable columns.

preserve

Use keep_original_cols instead to specify whether the selected column(s) should be retained in addition to the new dummy variables.

columns

A character vector for the selected columns. This is NULL until the step is trained by recipes::prep().

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 bake()? While all operations are baked when 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.

Details

step_feature_hash() will create a set of binary dummy variables from a factor or character variable. The values themselves are used to determine which row that the dummy variable should be assigned (as opposed to having a specific column that the value will map to).

Since this method does not rely on a pre-determined assignment of levels to columns, new factor levels can be added to the selected columns without issue. Missing values result in missing values for all of the hashed columns.

Note that the assignment of the levels to the hashing columns does not try to maximize the allocation. It is likely that multiple levels of the column will map to the same hashed columns (even with small data sets). Similarly, it is likely that some columns will have all zeros. A zero-variance filter (via recipes::step_zv()) is recommended for any recipe that uses hashed columns.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

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

terms

character, the selectors or variables selected

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

References

Weinberger, K, A Dasgupta, J Langford, A Smola, and J Attenberg. 2009. "Feature Hashing for Large Scale Multitask Learning." In Proceedings of the 26th Annual International Conference on Machine Learning, 1113–20. ACM.

Kuhn and Johnson (2020) Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC/Chapman Hall https://bookdown.org/max/FES/encoding-predictors-with-many-categories.html

See Also

recipes::step_dummy(), recipes::step_zv()

Examples

data(grants, package = "modeldata")
rec <-
  recipe(class ~ sponsor_code, data = grants_other) %>%
  step_feature_hash(
    sponsor_code,
    num_hash = 2^6, keep_original_cols = TRUE
  ) %>%
  prep()

# How many of the 298 locations ended up in each hash column?
results <-
  bake(rec, new_data = NULL, starts_with("sponsor_code")) %>%
  distinct()

apply(results %>% select(-sponsor_code), 2, sum) %>% table()