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lgb.prepare_rules2.R
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lgb.prepare_rules2.R
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#' Data preparator for LightGBM datasets with rules (integer)
#'
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
#' Factors and characters are converted to numeric (specifically: integer).
#' In addition, keeps rules created so you can convert other datasets using this converter.
#' This is useful if you have a specific need for integer dataset instead of numeric dataset.
#' Note that there are programs which do not support integer-only input.
#' Consider this as a half memory technique which is dangerous, especially for LightGBM.
#'
#' @param data A data.frame or data.table to prepare.
#' @param rules A set of rules from the data preparator, if already used.
#'
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}).
#' The data must be converted to a matrix format (\code{as.matrix}) for input in
#' \code{lgb.Dataset}.
#'
#' @examples
#' library(lightgbm)
#' data(iris)
#'
#' str(iris)
#'
#' new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
#' str(new_iris$data)
#'
#' data(iris) # Erase iris dataset
#' iris$Species[1L] <- "NEW FACTOR" # Introduce junk factor (NA)
#'
#' # Use conversion using known rules
#' # Unknown factors become 0, excellent for sparse datasets
#' newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
#'
#' # Unknown factor is now zero, perfect for sparse datasets
#' newer_iris$data[1L, ] # Species became 0 as it is an unknown factor
#'
#' newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value
#'
#' # Is the newly created dataset equal? YES!
#' all.equal(new_iris$data, newer_iris$data)
#'
#' # Can we test our own rules?
#' data(iris) # Erase iris dataset
#'
#' # We remapped values differently
#' personal_rules <- list(
#' Species = c(
#' "setosa" = 3L
#' , "versicolor" = 2L
#' , "virginica" = 1L
#' )
#' )
#' newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
#' str(newest_iris$data) # SUCCESS!
#'
#' @importFrom data.table set
#' @export
lgb.prepare_rules2 <- function(data, rules = NULL) {
# data.table not behaving like data.frame
if (inherits(data, "data.table")) {
# Must use existing rules
if (!is.null(rules)) {
# Loop through rules
for (i in names(rules)) {
data.table::set(data, j = i, value = unname(rules[[i]][data[[i]]]))
data[[i]][is.na(data[[i]])] <- 0L # Overwrite NAs by 0s as integer
}
} else {
# Get data classes
list_classes <- vapply(data, class, character(1L))
# Map characters/factors
is_fix <- which(list_classes %in% c("character", "factor"))
rules <- list()
# Need to create rules?
if (length(is_fix) > 0L) {
# Go through all characters/factors
for (i in is_fix) {
# Store column elsewhere
mini_data <- data[[i]]
# Get unique values
if (is.factor(mini_data)) {
mini_unique <- levels(mini_data) # Factor
mini_numeric <- seq_along(mini_unique) # Respect ordinal if needed
} else {
mini_unique <- as.factor(unique(mini_data)) # Character
mini_numeric <- as.integer(mini_unique) # No respect of ordinality
}
# Create rules
indexed <- colnames(data)[i] # Index value
rules[[indexed]] <- mini_numeric # Numeric content
names(rules[[indexed]]) <- mini_unique # Character equivalent
# Apply to real data column
data.table::set(data, j = i, value = unname(rules[[indexed]][mini_data]))
}
}
}
} else {
# Must use existing rules
if (!is.null(rules)) {
# Loop through rules
for (i in names(rules)) {
data[[i]] <- unname(rules[[i]][data[[i]]])
data[[i]][is.na(data[[i]])] <- 0L # Overwrite NAs by 0s as integer
}
} else {
# Default routine (data.frame)
if (inherits(data, "data.frame")) {
# Get data classes
list_classes <- vapply(data, class, character(1L))
# Map characters/factors
is_fix <- which(list_classes %in% c("character", "factor"))
rules <- list()
# Need to create rules?
if (length(is_fix) > 0L) {
# Go through all characters/factors
for (i in is_fix) {
# Store column elsewhere
mini_data <- data[[i]]
# Get unique values
if (is.factor(mini_data)) {
mini_unique <- levels(mini_data) # Factor
mini_numeric <- seq_along(mini_unique) # Respect ordinal if needed
} else {
mini_unique <- as.factor(unique(mini_data)) # Character
mini_numeric <- as.integer(mini_unique) # No respect of ordinality
}
# Create rules
indexed <- colnames(data)[i] # Index value
rules[[indexed]] <- mini_numeric # Numeric content
names(rules[[indexed]]) <- mini_unique # Character equivalent
# Apply to real data column
data[[i]] <- unname(rules[[indexed]][mini_data])
}
}
} else {
# What do you think you are doing here? Throw error.
stop(
"lgb.prepare: you provided "
, paste(class(data), collapse = " & ")
, " but data should have class data.frame"
)
}
}
}
return(list(data = data, rules = rules))
}