Encoding Factors into Multiple Columns

step_embed() creates a specification of a recipe step that will convert a nominal (i.e. factor) predictor into a set of scores derived from a tensorflow model via a word-embedding model. embed_control is a simple wrapper for setting default options.

Usage

step_embed(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  outcome = NULL,
  predictors = NULL,
  num_terms = 2,
  hidden_units = 0,
  options = embed_control(),
  mapping = NULL,
  history = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("embed")
)

embed_control(
  loss = "mse",
  metrics = NULL,
  optimizer = "sgd",
  epochs = 20,
  validation_split = 0,
  batch_size = 32,
  verbose = 0,
  callbacks = NULL
)

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 step_embed, this indicates the variables to be encoded into a numeric format. See recipes::selections() for more details. For the tidy method, these are not currently used.
role: For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the embedding variables created will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
outcome: A call to vars to specify which variable is used as the outcome in the neural network.
predictors: An optional call to vars to specify any variables to be added as additional predictors in the neural network. These variables should be numeric and perhaps centered and scaled.
num_terms: An integer for the number of resulting variables.
hidden_units: An integer for the number of hidden units in a dense ReLu layer between the embedding and output later. Use a value of zero for no intermediate layer (see Details below).
options: A list of options for the model fitting process.
mapping: A list of tibble results that define the encoding. This is NULL until the step is trained by recipes::prep().
history: A tibble with the convergence statistics for each term. 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 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.
optimizer, loss, metrics: Arguments to pass to keras::compile()
epochs, validation_split, batch_size, verbose, callbacks: Arguments to pass to keras::fit()

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the selectors or variables for encoding), level (the factor levels), and several columns containing embed in the name.

Details

Factor levels are initially assigned at random to the new variables and these variables are used in a neural network to optimize both the allocation of levels to new columns as well as estimating a model to predict the outcome. See Section 6.1.2 of Francois and Allaire (2018) for more details.

The new variables are mapped to the specific levels seen at the time of model training and an extra instance of the variables are used for new levels of the factor.

One model is created for each call to step_embed. All terms given to the step are estimated and encoded in the same model which would also contain predictors give in predictors (if any).

When the outcome is numeric, a linear activation function is used in the last layer while softmax is used for factor outcomes (with any number of levels).

For example, the keras code for a numeric outcome, one categorical predictor, and no hidden units used here would be

  keras_model_sequential() %>%
  layer_embedding(
    input_dim = num_factor_levels_x + 1,
    output_dim = num_terms,
    input_length = 1
  ) %>%
  layer_flatten() %>%
  layer_dense(units = 1, activation = 'linear')

If a factor outcome is used and hidden units were requested, the code would be

  keras_model_sequential() %>%
  layer_embedding(
    input_dim = num_factor_levels_x + 1,
    output_dim = num_terms,
    input_length = 1
   ) %>%
  layer_flatten() %>%
  layer_dense(units = hidden_units, activation = "relu") %>%
  layer_dense(units = num_factor_levels_y, activation = 'softmax')

Other variables specified by predictors are added as an additional dense layer after layer_flatten and before the hidden layer.

Also note that it may be difficult to obtain reproducible results using this step due to the nature of Tensorflow (see link in References).

tensorflow models cannot be run in parallel within the same session (via foreach or futures) or the parallel package. If using a recipes with this step with caret, avoid parallel processing.

Tidying

When you tidy() this step, a tibble is retruned with a number of columns with embedding information, and columns terms, levels, and id:

terms: character, the selectors or variables selected
levels: character, levels in variable
id: character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

num_terms: # Model Terms (type: integer, default: 2)
hidden_units: # Hidden Units (type: integer, default: 0)

Case weights

The underlying operation does not allow for case weights.

References

Francois C and Allaire JJ (2018) Deep Learning with R, Manning

"Concatenate Embeddings for Categorical Variables with Keras" https://flovv.github.io/Embeddings_with_keras_part2/

Examples

data(grants, package = "modeldata")

set.seed(1)
grants_other <- sample_n(grants_other, 500)

rec <- recipe(class ~ num_ci + sponsor_code, data = grants_other) %>%
  step_embed(sponsor_code,
    outcome = vars(class),
    options = embed_control(epochs = 10)
  )