#' Compute feature importance in a model
#'
#' Creates a \code{data.table} of feature importances in a model.
#'
#' @param model object of class \code{lgb.Booster}.
#' @param percentage whether to show importance in relative percentage.
#'
#' @return
#'
#' For a tree model, a \code{data.table} with the following columns:
#' \itemize{
#'   \item \code{Feature} Feature names in the model.
#'   \item \code{Gain} The total gain of this feature's splits.
#'   \item \code{Cover} The number of observation related to this feature.
#'   \item \code{Frequency} The number of times a feature splited in trees.
#' }
#'
#' @examples
#'
#' 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_imp1 <- lgb.importance(model, percentage = TRUE)
#' tree_imp2 <- lgb.importance(model, percentage = FALSE)
#'
#' @importFrom magrittr %>% %T>%
#' @importFrom data.table :=
#' @export

lgb.importance <- function(model, percentage = TRUE) {
  if (!any(class(model) == "lgb.Booster")) {
    stop("'model' has to be an object of class lgb.Booster")
  }
  tree_dt <- lgb.model.dt.tree(model)
  tree_imp <- tree_dt %>%
    magrittr::extract(.,
                      i = is.na(split_index) == FALSE,
                      j = .(Gain = sum(split_gain), Cover = sum(internal_count), Frequency = .N),
                      by = "split_feature") %T>%
    data.table::setnames(., old = "split_feature", new = "Feature") %>%
    magrittr::extract(., i = order(Gain, decreasing = TRUE))
  if (percentage) {
    tree_imp[, ":="(Gain = Gain / sum(Gain),
                    Cover = Cover / sum(Cover),
                    Frequency = Frequency / sum(Frequency))]
  }
  return(tree_imp)
}