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#' 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.
#'
#' @param recipe A recipe object. The step will be added to the sequence of
#' operations for this recipe.
#' @param ... 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.
#' @param 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.
#' @param outcome A call to `vars` to specify which variable is used as the
#' outcome in the neural network.
#' @param 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.
#' @param num_terms An integer for the number of resulting variables.
#' @param 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).
#' @param options A list of options for the model fitting process.
#' @param mapping A list of tibble results that define the encoding. This is
#' `NULL` until the step is trained by [recipes::prep()].
#' @param history A tibble with the convergence statistics for each term. This
#' is `NULL` until the step is trained by [recipes::prep()].
#' @param keep_original_cols A logical to keep the original variables in the
#' output. Defaults to `FALSE`.
#' @param 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.
#' @param trained A logical to indicate if the quantities for preprocessing have
#' been estimated.
#' @param id A character string that is unique to this step to identify it.
#' @return 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.
#' @keywords datagen
#' @concept preprocessing encoding
#' @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()`][tidy.recipe()] this step, a tibble is retruned with
#' a number of columns with embedding information, and columns `terms`,
#' `levels`, and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{levels}{character, levels in variable}
#' \item{id}{character, id of this step}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_embed"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-not-supported
#'
#' @references
#'
#' Francois C and Allaire JJ (2018) _Deep Learning with R_, Manning
#'
#' "Concatenate Embeddings for Categorical Variables with Keras"
#' \url{https://flovv.github.io/Embeddings_with_keras_part2/}
#'
#' @examplesIf !embed:::is_cran_check() && rlang::is_installed(c("modeldata", "keras"))
#' 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)
#' )
#' @export
step_embed <-
function(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")) {
# warm start for tf to avoid a bug in tensorflow
is_tf_available()
if (is.null(outcome)) {
rlang::abort("Please list a variable in `outcome`")
}
add_step(
recipe,
step_embed_new(
terms = enquos(...),
role = role,
trained = trained,
outcome = outcome,
predictors = predictors,
num_terms = num_terms,
hidden_units = hidden_units,
options = options,
mapping = mapping,
history = history,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_embed_new <-
function(terms, role, trained, outcome, predictors, num_terms, hidden_units,
options, mapping, history, keep_original_cols, skip, id) {
step(
subclass = "embed",
terms = terms,
role = role,
num_terms = num_terms,
hidden_units = hidden_units,
options = options,
trained = trained,
outcome = outcome,
predictors = predictors,
mapping = mapping,
history = history,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
#' @export
prep.step_embed <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
if (length(col_names) > 0) {
check_type(training[, col_names], types = c("string", "factor", "ordered"))
y_name <- recipes_eval_select(x$outcome, training, info)
if (length(x$predictors) > 0) {
pred_names <- recipes_eval_select(x$predictors, training, info)
check_type(training[, pred_names], types = c("double", "integer"))
} else {
pred_names <- NULL
}
x$options <- tf_options_check(x$options)
res <-
tf_coefs2(
x = training[, col_names],
y = training[, y_name],
z = if (is.null(pred_names)) NULL else training[, pred_names],
opt = x$options,
num = x$num_terms,
h = x$hidden_units
)
# compute epochs actually trained for
epochs <- min(res$history$params$epochs, length(res$history$metrics[[1]]))
.hist <- # TODO convert to pivot and get signature for below
as_tibble(res$history$metrics) %>%
mutate(epochs = 1:epochs) %>%
tidyr::pivot_longer(c(-epochs), names_to = "type", values_to = "loss")
} else {
res <- NULL
.hist <- tibble::tibble(
epochs = integer(0),
type = character(0),
loss = numeric(0)
)
}
step_embed_new(
terms = x$terms,
role = x$role,
trained = TRUE,
outcome = x$outcome,
predictors = x$predictors,
num_terms = x$num_terms,
hidden_units = x$hidden_units,
options = x$options,
mapping = res$layer_values,
history = .hist,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
is_tf_2 <- function() {
if (!is_tf_available()) {
rlang::abort(
c(
"tensorflow could now be found.",
"Please run `tensorflow::install_tensorflow()` to install."
)
)
}
compareVersion("2.0", as.character(tensorflow::tf_version())) <= 0
}
tf_coefs2 <- function(x, y, z, opt, num, lab, h, seeds = sample.int(10000, 4),
...) {
rlang::check_installed("keras")
vars <- names(x)
p <- length(vars)
set.seed(seeds[1])
if (is_tf_2()) {
tensorflow::tf$random$set_seed(seeds[2])
} else {
tensorflow::use_session_with_seed(seeds[2])
}
on.exit(keras::backend()$clear_session())
lvl <- lapply(x, levels)
# convert levels to integers; zero signifies a new level
mats <- lapply(x, function(x) matrix(as.numeric(x), ncol = 1))
y <- y[[1]]
if (is.character(y)) {
y <- as.factor(y)
}
factor_y <- is.factor(y)
if (factor_y) {
y <- class2ind(y)
} else {
y <- matrix(y, ncol = 1)
}
inputs <- vector(mode = "list", length = p)
# For each categorical predictor, make an input layer
for (i in 1:p) {
inputs[[i]] <- keras::layer_input(shape = 1, name = paste0("input_", vars[i]))
}
layers <- vector(mode = "list", length = p)
# Now add embedding to each layer and then flatten
for (i in 1:p) {
layers[[i]] <-
inputs[[i]] %>%
keras::layer_embedding(
input_dim = length(lvl[[i]]) + 1,
output_dim = num,
input_length = 1,
name = paste0("layer_", vars[i])
) %>%
keras::layer_flatten()
}
if (is.null(z)) {
if (p > 1) {
all_layers <- keras::layer_concatenate(layers)
} else {
all_layers <- layers[[1]]
}
} else {
mats$z <- as.matrix(z)
pred_layer <- keras::layer_input(shape = ncol(z), name = "other_pred")
all_layers <- keras::layer_concatenate(c(layers, pred_layer))
inputs <- c(inputs, pred_layer)
}
if (h > 0) {
all_layers <-
all_layers %>%
keras::layer_dense(
units = h, activation = "relu", name = "hidden_layer",
kernel_initializer = keras::initializer_glorot_uniform(seed = seeds[3])
)
}
if (factor_y) {
all_layers <-
all_layers %>%
keras::layer_dense(
units = ncol(y), activation = "softmax", name = "output_layer",
kernel_initializer = keras::initializer_glorot_uniform(seed = seeds[4])
)
} else {
all_layers <-
all_layers %>%
keras::layer_dense(
units = 1, activation = "linear", name = "output_layer",
kernel_initializer = keras::initializer_glorot_uniform(seed = seeds[4])
)
}
model <-
keras::keras_model(inputs = inputs, outputs = all_layers)
model %>%
keras::compile(
loss = opt$loss,
metrics = opt$metrics,
optimizer = opt$optimizer
)
history <-
model %>%
keras::fit(
x = unname(mats),
y = y,
epochs = opt$epochs,
validation_split = opt$validation_split,
batch_size = opt$batch_size,
verbose = opt$verbose,
callbacks = opt$callbacks
)
layer_values <- vector(mode = "list", length = p)
for (i in 1:p) {
layer_values[[i]] <-
keras::get_layer(model, paste0("layer_", vars[i]))$get_weights() %>%
as.data.frame() %>%
setNames(names0(num, paste0(vars[i], "_embed_"))) %>%
as_tibble() %>%
mutate(..level = c("..new", lvl[[i]]))
}
names(layer_values) <- vars
list(layer_values = layer_values, history = history)
}
map_tf_coef2 <- function(dat, mapping, prefix) {
new_val <- mapping %>%
dplyr::filter(..level == "..new") %>%
dplyr::select(-..level)
dat <- dat %>%
mutate(..order = seq_len(nrow(dat))) %>%
set_names(c("..level", "..order")) %>%
mutate(..level = as.character(..level))
mapping <- mapping %>% dplyr::filter(..level != "..new")
dat <- left_join(dat, mapping, by = "..level") %>%
arrange(..order)
dat <- dat %>% dplyr::select(contains("_embed"))
dat[!complete.cases(dat), ] <- new_val
dat
}
#' @export
bake.step_embed <- function(object, new_data, ...) {
col_names <- names(object$mapping)
check_new_data(col_names, object, new_data)
for (col_name in col_names) {
tmp <- map_tf_coef2(
dat = new_data[, col_name], # map_tf_coef2() expects a tibble
mapping = object$mapping[[col_name]],
prefix = col_name
)
tmp <- check_name(tmp, new_data, object, names(tmp))
new_data <- vec_cbind(new_data, tmp)
}
new_data <- remove_original_cols(new_data, object, col_names)
new_data
}
#' @rdname step_embed
#' @usage NULL
#' @export
tidy.step_embed <- function(x, ...) {
if (is_trained(x)) {
if (length(x$mapping) == 0) {
res <- tibble(
terms = character(),
level = character(),
value = double()
)
} else {
for (i in seq_along(x$mapping)) {
x$mapping[[i]]$terms <- names(x$mapping)[i]
}
res <- bind_rows(x$mapping)
names(res) <- gsub("^\\.\\.", "", names(res))
}
} else {
term_names <- sel2char(x$terms)
res <- tibble(
terms = term_names,
level = rep(na_chr, length(term_names)),
value = rep(na_dbl, length(term_names))
)
}
res$id <- x$id
res
}
#' @export
print.step_embed <-
function(x, width = max(20, options()$width - 31), ...) {
title <- "Embedding of factors via tensorflow for "
print_step(names(x$mapping), x$terms, x$trained, title, width)
invisible(x)
}
#' @export
#' @rdname step_embed
#' @param optimizer,loss,metrics Arguments to pass to keras::compile()
#' @param epochs,validation_split,batch_size,verbose,callbacks Arguments to pass
#' to keras::fit()
embed_control <- function(loss = "mse",
metrics = NULL,
optimizer = "sgd",
epochs = 20,
validation_split = 0,
batch_size = 32,
verbose = 0,
callbacks = NULL) {
if (batch_size < 1) {
rlang::abort("`batch_size` should be a positive integer")
}
if (epochs < 1) {
rlang::abort("`epochs` should be a positive integer")
}
if (validation_split < 0 || validation_split > 1) {
rlang::abort("`validation_split` should be on [0, 1)")
}
list(
loss = loss, metrics = metrics, optimizer = optimizer, epochs = epochs,
validation_split = validation_split, batch_size = batch_size,
verbose = verbose, callbacks = callbacks
)
}
tf_options_check <- function(opt) {
exp_names <- c(
"loss",
"metrics",
"optimizer",
"epochs",
"validation_split",
"batch_size",
"verbose"
)
if (length(setdiff(exp_names, names(opt))) > 0) {
rlang::abort(
paste0(
"The following options are missing from the `options`: ",
paste0(setdiff(exp_names, names(opt)), collapse = ",")
)
)
}
opt
}
class2ind <- function(x) {
if (!is.factor(x)) {
rlang::abort("'x' should be a factor")
}
y <- model.matrix(~ x - 1)
colnames(y) <- gsub("^x", "", colnames(y))
attributes(y)$assign <- NULL
attributes(y)$contrasts <- NULL
y
}
is_tf_available <- function() {
if (!rlang::is_installed("tensorflow")) {
return(FALSE)
}
capture.output(
res <- try(tensorflow::tf_config(), silent = TRUE),
file = NULL
)
if (inherits(res, "try-error") || all(is.null(res))) {
return(FALSE)
} else {
if (!(any(names(res) == "available"))) {
return(FALSE)
}
}
res$available
}
#' @rdname required_pkgs.embed
#' @export
required_pkgs.step_embed <- function(x, ...) {
c("keras", "embed")
}
#' @export
#' @rdname tunable_embed
tunable.step_embed <- function(x, ...) {
tibble::tibble(
name = c("num_terms", "hidden_units"),
call_info = list(
list(pkg = "dials", fun = "num_terms", range = c(2, 10)),
list(pkg = "dials", fun = "hidden_units", range = c(0, 10))
),
source = "recipe",
component = "step_embed",
component_id = x$id
)
}
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