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#' Non-negative matrix factorization signal extraction with lasso penalization
#'
#' `step_nnmf_sparse()` creates a *specification* of a recipe step that will
#' convert numeric data into one or more non-negative components.
#'
#' @inheritParams step_pca
#' @inheritParams step_center
#' @param penalty A non-negative number used as a penalization factor for the
#' loadings. Values are usually between zero and one.
#' @param options A list of options to `nmf()` in the RcppML package. That
#' package has a separate function `setRcppMLthreads()` that controls the
#' amount of internal parallelization. **Note** that the argument `A`, `k`,
#' `L1`, and `seed` should not be passed here.
#' @param res A matrix of loadings is stored here, along with the names of the
#' original predictors, once this preprocessing step has been trained by
#' [prep()].
#' @param seed An integer that will be used to set the seed in isolation when
#' computing the factorization.
#' @template step-return
#' @family multivariate transformation steps
#' @export
#' @details Non-negative matrix factorization computes latent components that
#' have non-negative values and take into account that the original data have
#' non-negative values.
#'
#' ```{r, echo = FALSE, results="asis"}
#' prefix <- "NNMF"
#' result <- knitr::knit_child("man/rmd/num_comp.Rmd")
#' cat(result)
#' ```
#'
#' # Tidying
#'
#' When you [`tidy()`][tidy.recipe()] this step, a tibble is returned with
#' columns `terms`, `value`, `component` , and `id`:
#'
#' \describe{
#' \item{terms}{character, the selectors or variables selected}
#' \item{value}{numeric, value of loading}
#' \item{component}{character, name of component}
#' \item{id}{character, id of this step}
#' }
#'
#' ```{r, echo = FALSE, results="asis"}
#' step <- "step_nnmf_sparse"
#' result <- knitr::knit_child("man/rmd/tunable-args.Rmd")
#' cat(result)
#' ```
#'
#' @template case-weights-not-supported
#'
#' @examplesIf .Platform$OS.type!= "windows"
#' if (rlang::is_installed(c("modeldata", "RcppML", "ggplot2"))) {
#' library(Matrix)
#' data(biomass, package = "modeldata")
#'
#' rec <- recipe(HHV ~ ., data = biomass) %>%
#' update_role(sample, new_role = "id var") %>%
#' update_role(dataset, new_role = "split variable") %>%
#' step_nnmf_sparse(
#' all_numeric_predictors(),
#' num_comp = 2,
#' seed = 473,
#' penalty = 0.01
#' ) %>%
#' prep(training = biomass)
#'
#' bake(rec, new_data = NULL)
#'
#' library(ggplot2)
#' bake(rec, new_data = NULL) %>%
#' ggplot(aes(x = NNMF2, y = NNMF1, col = HHV)) +
#' geom_point()
#' }
step_nnmf_sparse <-
function(recipe,
...,
role = "predictor",
trained = FALSE,
num_comp = 2,
penalty = 0.001,
options = list(),
res = NULL,
prefix = "NNMF",
seed = sample.int(10^5, 1),
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("nnmf_sparse")) {
recipes_pkg_check(required_pkgs.step_nnmf_sparse())
add_step(
recipe,
step_nnmf_sparse_new(
terms = enquos(...),
role = role,
trained = trained,
num_comp = num_comp,
penalty = penalty,
options = options,
res = res,
prefix = prefix,
seed = seed,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
)
}
step_nnmf_sparse_new <-
function(terms, role, trained, num_comp, penalty, options, res,
prefix, seed, keep_original_cols, skip, id) {
step(
subclass = "nnmf_sparse",
terms = terms,
role = role,
trained = trained,
num_comp = num_comp,
penalty = penalty,
options = options,
res = res,
prefix = prefix,
seed = seed,
keep_original_cols = keep_original_cols,
skip = skip,
id = id
)
}
tibble_to_sparse <- function(x, transp = FALSE) {
x <- as.matrix(x)
if (transp) {
x <- t(x)
}
Matrix::Matrix(x, sparse = TRUE)
}
nnmf_pen_call <- function(x) {
opts <-
list(
A = expr(dat),
k = x$num_comp,
L1 = c(x$penalty, x$penalty),
verbose = FALSE,
seed = x$seed,
nonneg = TRUE
)
cl <- rlang::call2("nmf", .ns = "RcppML", !!!opts)
user_opts <- x$opt
cl <- rlang::call_modify(cl, !!!user_opts)
cl
}
#' @export
prep.step_nnmf_sparse <- function(x, training, info = NULL, ...) {
col_names <- recipes_eval_select(x$terms, training, info)
check_type(training[, col_names], types = c("double", "integer"))
if (x$num_comp > 0 && length(col_names) > 0) {
x$num_comp <- min(x$num_comp, length(col_names))
dat <- tibble_to_sparse(training[, col_names], transp = TRUE)
cl <- nnmf_pen_call(x)
nnm <- try(rlang::eval_tidy(cl), silent = TRUE)
if (inherits(nnm, "try-error")) {
cli::cli_abort(c(
x = "Failed with error:",
i = as.character(nnm)
))
} else {
na_w <- sum(is.na(nnm$w))
if (na_w > 0) {
cli::cli_abort(
"The NNMF loadings are missing. The penalty may have been to high."
)
} else {
nnm <- list(x_vars = col_names, w = nnm$w)
rownames(nnm$w) <- col_names
colnames(nnm$w) <- names0(ncol(nnm$w), x$prefix)
}
}
} else {
nnm <- list(x_vars = col_names, w = NULL)
x$num_comp <- 0
}
step_nnmf_sparse_new(
terms = x$terms,
role = x$role,
trained = TRUE,
num_comp = x$num_comp,
penalty = x$penalty,
options = x$options,
res = nnm,
prefix = x$prefix,
seed = x$seed,
keep_original_cols = get_keep_original_cols(x),
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_nnmf_sparse <- function(object, new_data, ...) {
check_new_data(object$res$x_vars, object, new_data)
if (object$num_comp == 0) {
return(new_data)
}
proj_data <- as.matrix(new_data[, object$res$x_vars, drop = FALSE])
proj_data <- proj_data %*% object$res$w
colnames(proj_data) <- names0(ncol(proj_data), object$prefix)
proj_data <- as_tibble(proj_data)
proj_data <- check_name(proj_data, new_data, object)
new_data <- vec_cbind(new_data, proj_data)
new_data <- remove_original_cols(new_data, object, object$res$x_vars)
new_data
}
#' @export
print.step_nnmf_sparse <- function(x, width = max(20, options()$width - 29), ...) {
if (x$trained) {
if (x$num_comp == 0) {
title <- "No non-negative matrix factorization was extracted from "
} else {
title <- "Non-negative matrix factorization for "
}
columns <- names(x$res$x_vars)
} else {
title <- "Non-negative matrix factorization for "
}
print_step(columns, x$terms, x$trained, title, width)
invisible(x)
}
#' @rdname tidy.recipe
#' @param x A `step_nnmf_sparse` object.
tidy.step_nnmf_sparse <- function(x, ...) {
if (is_trained(x)) {
if (x$num_comp > 0) {
res <- x$res$w
var_nms <- rownames(res)
res <- tibble::as_tibble(res)
res$terms <- var_nms
res <- tidyr::pivot_longer(res,
cols = c(-terms),
names_to = "component", values_to = "value"
)
res <- res[, c("terms", "value", "component")]
res <- res[order(res$component, res$terms), ]
} else {
res <- tibble(
terms = unname(x$res$x_vars),
value = na_dbl,
component = na_chr
)
}
} else {
term_names <- sel2char(x$terms)
res <- tibble(terms = term_names, value = na_dbl, component = na_chr)
}
res$id <- x$id
res
}
# ------------------------------------------------------------------------------
#' @export
tunable.step_nnmf_sparse <- function(x, ...) {
tibble::tibble(
name = c("num_comp", "penalty"),
call_info = list(
list(pkg = "dials", fun = "num_comp", range = c(1L, 4L)),
list(pkg = "dials", fun = "penalty")
),
source = "recipe",
component = "step_nnmf_sparse",
component_id = x$id
)
}
#' @rdname required_pkgs.recipe
#' @export
required_pkgs.step_nnmf_sparse <- function(x, ...) {
c("Matrix", "RcppML")
}
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