wehub-resource-sync
100 lines
3.1 KiB
R
100 lines
3.1 KiB
R
#' @name lgb.plot.importance
|
|
#' @title Plot feature importance as a bar graph
|
|
#' @description Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.
|
|
#' @param tree_imp a \code{data.table} returned by \code{\link{lgb.importance}}.
|
|
#' @param top_n maximal number of top features to include into the plot.
|
|
#' @param measure the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".
|
|
#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.
|
|
#' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{\link[graphics]{barplot}}.
|
|
#' Set a number smaller than 1.0 to make the bar labels smaller than R's default and values
|
|
#' greater than 1.0 to make them larger.
|
|
#'
|
|
#' @details
|
|
#' The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature.
|
|
#' Features are shown ranked in a decreasing importance order.
|
|
#'
|
|
#' @return
|
|
#' The \code{lgb.plot.importance} function creates a \code{barplot}
|
|
#' and silently returns a processed data.table with \code{top_n} features sorted by defined importance.
|
|
#'
|
|
#' @examples
|
|
#' \donttest{
|
|
#' \dontshow{setLGBMthreads(2L)}
|
|
#' \dontshow{data.table::setDTthreads(1L)}
|
|
#' data(agaricus.train, package = "lightgbm")
|
|
#' train <- agaricus.train
|
|
#' dtrain <- lgb.Dataset(train$data, label = train$label)
|
|
#'
|
|
#' params <- list(
|
|
#' objective = "binary"
|
|
#' , learning_rate = 0.1
|
|
#' , min_data_in_leaf = 1L
|
|
#' , min_sum_hessian_in_leaf = 1.0
|
|
#' , num_threads = 2L
|
|
#' )
|
|
#'
|
|
#' model <- lgb.train(
|
|
#' params = params
|
|
#' , data = dtrain
|
|
#' , nrounds = 5L
|
|
#' )
|
|
#'
|
|
#' tree_imp <- lgb.importance(model, percentage = TRUE)
|
|
#' lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
|
|
#' }
|
|
#' @importFrom graphics barplot par
|
|
#' @export
|
|
lgb.plot.importance <- function(tree_imp,
|
|
top_n = 10L,
|
|
measure = "Gain",
|
|
left_margin = 10L,
|
|
cex = NULL
|
|
) {
|
|
|
|
# Check for measurement (column names) correctness
|
|
measure <- match.arg(
|
|
measure
|
|
, choices = c("Gain", "Cover", "Frequency")
|
|
, several.ok = FALSE
|
|
)
|
|
|
|
# Get top N importance (defaults to 10)
|
|
top_n <- min(top_n, nrow(tree_imp))
|
|
|
|
# Parse importance
|
|
tree_imp <- tree_imp[order(abs(get(measure)), decreasing = TRUE), ][seq_len(top_n), ]
|
|
|
|
# Attempt to setup a correct cex
|
|
if (is.null(cex)) {
|
|
cex <- 2.5 / log2(1.0 + top_n)
|
|
}
|
|
|
|
# Refresh plot
|
|
op <- graphics::par(no.readonly = TRUE)
|
|
on.exit(graphics::par(op))
|
|
|
|
graphics::par(
|
|
mar = c(
|
|
op$mar[1L]
|
|
, left_margin
|
|
, op$mar[3L]
|
|
, op$mar[4L]
|
|
)
|
|
)
|
|
|
|
tree_imp[rev(seq_len(.N)),
|
|
graphics::barplot(
|
|
height = get(measure)
|
|
, names.arg = Feature
|
|
, horiz = TRUE
|
|
, border = NA
|
|
, main = "Feature Importance"
|
|
, xlab = measure
|
|
, cex.names = cex
|
|
, las = 1L
|
|
)]
|
|
|
|
return(invisible(tree_imp))
|
|
|
|
}
|