#' Plot feature importance as a bar graph
#' 
#' 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{barplot}.
#' 
#' @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
#' \dontrun{
#' data(agaricus.train, package = "lightgbm")
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#'
#' params = list(objective = "binary",
#'               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
#'               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
#'               model <- lgb.train(params, dtrain, 20)
#' model <- lgb.train(params, dtrain, 20)
#'
#' tree_imp <- lgb.importance(model, percentage = TRUE)
#' lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")
#' }
#' 
#' @export
lgb.plot.importance <- function(tree_imp,
                                top_n = 10,
                                measure = "Gain",
                                left_margin = 10,
                                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),][1:top_n,]
  
  # Attempt to setup a correct cex
  if (is.null(cex)) {
    cex <- 2.5 / log2(1 + top_n)
  }
  
  # Refresh plot
  op <- par(no.readonly = TRUE)
  on.exit(par(op))
  
  # Do some magic plotting
  par(mar = op$mar %>% magrittr::inset(., 2, left_margin))
  
  # Do plot
  tree_imp[.N:1,
           barplot(height = get(measure),
                   names.arg = Feature,
                   horiz = TRUE,
                   border = NA,
                   main = "Feature Importance",
                   xlab = measure,
                   cex.names = cex,
                   las = 1)]
  
  # Return invisibly
  invisible(tree_imp)
  
}