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#' Linear Combination Filter
#'
#' `step_lincomb` creates a *specification* of a recipe
#' step that will potentially remove numeric variables that have
#' linear combinations between them.
#'
#' @inheritParams step_center
#' @param ... One or more selector functions to choose which
#' variables are affected by the step. See [selections()]
#' for more details. For the `tidy` method, these are not
#' currently used.
#' @param role Not used by this step since no new variables are
#' created.
#' @param max_steps A value.
#' @param removals A character string that contains the names of
#' columns that should be removed. These values are not determined
#' until [prep.recipe()] is called.
#' @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` which
#' is the columns that will be removed.
#' @keywords datagen
#' @concept preprocessing
#' @concept variable_filters
#' @author Max Kuhn, Kirk Mettler, and Jed Wing
#' @export
#'
#' @details This step finds exact linear combinations between two
#' or more variables and recommends which column(s) should be
#' removed to resolve the issue. This algorithm may need to be
#' applied multiple times (as defined by `max_steps`).
#' @examples
#' library(modeldata)
#' data(biomass)
#'
#' biomass$new_1 <- with(biomass,
#' .1*carbon - .2*hydrogen + .6*sulfur)
#' biomass$new_2 <- with(biomass,
#' .5*carbon - .2*oxygen + .6*nitrogen)
#'
#' biomass_tr <- biomass[biomass$dataset == "Training",]
#' biomass_te <- biomass[biomass$dataset == "Testing",]
#'
#' rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen +
#' sulfur + new_1 + new_2,
#' data = biomass_tr)
#'
#' lincomb_filter <- rec %>%
#' step_lincomb(all_predictors())
#'
#' lincomb_filter_trained <- prep(lincomb_filter, training = biomass_tr)
#' lincomb_filter_trained
#'
#' tidy(lincomb_filter, number = 1)
#' tidy(lincomb_filter_trained, number = 1)
#' @seealso [step_nzv()][step_corr()]
#' [recipe()] [prep.recipe()]
#' [bake.recipe()]
step_lincomb <-
function(recipe,
...,
role = NA,
trained = FALSE,
max_steps = 5,
removals = NULL,
skip = FALSE,
id = rand_id("lincomb")) {
add_step(
recipe,
step_lincomb_new(
terms = ellipse_check(...),
role = role,
trained = trained,
max_steps = max_steps,
removals = removals,
skip = skip,
id = id
)
)
}
step_lincomb_new <-
function(terms, role, trained, max_steps, removals, skip, id) {
step(
subclass = "lincomb",
terms = terms,
role = role,
trained = trained,
max_steps = max_steps,
removals = removals,
skip = skip,
id = id
)
}
#' @export
prep.step_lincomb <- function(x, training, info = NULL, ...) {
col_names <- eval_select_recipes(x$terms, training, info)
check_type(training[, col_names])
filter <- iter_lc_rm(x = training[, col_names],
max_steps = x$max_steps)
step_lincomb_new(
terms = x$terms,
role = x$role,
trained = TRUE,
max_steps = x$max_steps,
removals = filter,
skip = x$skip,
id = x$id
)
}
#' @export
bake.step_lincomb <- function(object, new_data, ...) {
if (length(object$removals) > 0)
new_data <- new_data[, !(colnames(new_data) %in% object$removals)]
as_tibble(new_data)
}
print.step_lincomb <-
function(x, width = max(20, options()$width - 36), ...) {
if (x$trained) {
if (length(x$removals) > 0) {
cat("Linear combination filter removed ")
cat(format_ch_vec(x$removals, width = width))
} else
cat("Linear combination filter removed no terms")
} else {
cat("Linear combination filter on ", sep = "")
cat(format_selectors(x$terms, width = width))
}
if (x$trained)
cat(" [trained]\n")
else
cat("\n")
invisible(x)
}
recommend_rm <- function(x, eps = 1e-6, ...) {
if (!is.matrix(x))
x <- as.matrix(x)
if (is.null(colnames(x)))
rlang::abort("`x` should have column names")
qr_decomp <- qr(x)
qr_decomp_R <- qr.R(qr_decomp) # extract R matrix
num_cols <- ncol(qr_decomp_R) # number of columns in R
rank <- qr_decomp$rank # number of independent columns
pivot <- qr_decomp$pivot # get the pivot vector
if (is.null(num_cols) || rank == num_cols) {
rm_list <- character(0) # there are no linear combinations
} else {
p1 <- 1:rank
X <- qr_decomp_R[p1, p1] # extract the independent columns
Y <- qr_decomp_R[p1, -p1, drop = FALSE] # extract the dependent columns
b <- qr(X) # factor the independent columns
b <- qr.coef(b, Y) # get regression coefficients of
# the dependent columns
b[abs(b) < eps] <- 0 # zap small values
# generate a list with one element for each dependent column
combos <- lapply(1:ncol(Y),
function(i)
c(pivot[rank + i], pivot[which(b[, i] != 0)]))
rm_list <- unlist(lapply(combos, function(x)
x[1]))
rm_list <- colnames(x)[rm_list]
}
rm_list
}
iter_lc_rm <- function(x,
max_steps = 10,
verbose = FALSE) {
if (is.null(colnames(x)))
rlang::abort("`x` should have column names")
orig_names <- colnames(x)
if (!is.matrix(x))
x <- as.matrix(x)
# converting to matrix may alter column names
name_df <- data.frame(orig = orig_names,
current = colnames(x),
stringsAsFactors = FALSE)
for (i in 1:max_steps) {
if (verbose)
cat(i)
if (i == max_steps)
break ()
lcs <- recommend_rm(x)
if (length(lcs) == 0)
break ()
else {
if (verbose)
cat(" removing", length(lcs), "\n")
x <- x[, !(colnames(x) %in% lcs)]
}
}
if (verbose)
cat("\n")
name_df <- name_df[!(name_df$current %in% colnames(x)), ]
name_df$orig
}
#' @rdname step_lincomb
#' @param x A `step_lincomb` object.
#' @export
tidy.step_lincomb <- tidy_filter
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