step_embed  R Documentation 
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 wordembedding model. embed_control
is a simple
wrapper for setting default options.
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
)
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

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 
predictors 
An optional call to 
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

history 
A tibble with the convergence statistics for each term. This
is 
keep_original_cols 
A logical to keep the original variables in the
output. Defaults to 
skip 
A logical. Should the step be skipped when the recipe is baked by

id 
A character string that is unique to this step to identify it. 
optimizer, loss, metrics 
Arguments to pass to 
epochs, validation_split, batch_size, verbose, callbacks 
Arguments to pass
to 
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.
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.
When you tidy()
this step, a tibble with columns terms
(the selectors or variables selected), levels
(levels in variable), and a
number of columns with embedding information are returned.
This step has 2 tuning parameters:
num_terms
: # Model Terms (type: integer, default: 2)
hidden_units
: # Hidden Units (type: integer, default: 0)
The underlying operation does not allow for case weights.
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/
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)
)
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