#' Plot feature contribution as a bar graph
#'
#' Plot previously calculated feature contribution as a bar graph.
#'
#' @param tree_interpretation_dt a \code{data.table} returned by \code{\link{lgb.interprete}}.
#' @param top_n maximal number of top features to include into the plot.
#' @param cols the column numbers of layout, will be used only for multiclass classification feature contribution.
#' @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 contribution of a feature.
#' Features are shown ranked in a decreasing contribution order.
#'
#' @return
#' The \code{lgb.plot.interpretation} function creates a \code{barplot}
#'
#' @examples
#'
#' Sigmoid <- function(x) 1 / (1 + exp(-x))
#' Logit <- function(x) log(x / (1 - x))
#' data(agaricus.train, package = 'lightgbm')
#' train <- agaricus.train
#' dtrain <- lgb.Dataset(train$data, label = train$label)
#' setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
#' data(agaricus.test, package = 'lightgbm')
#' test <- agaricus.test
#'
#' 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_interpretation <- lgb.interprete(model, test$data, 1:5)
#' lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10)
#'
#' @export

lgb.plot.interpretation <- function(tree_interpretation_dt, top_n = 10, cols = 1, left_margin = 10, cex = NULL) {
  num_class <- ncol(tree_interpretation_dt) - 1
  op <- par(no.readonly = TRUE)
  on.exit(par(op))
  par(mar = op$mar %>% magrittr::inset(., 1:3, c(3, left_margin, 2)))
  if (num_class == 1) {
    multiple.tree.plot.interpretation(tree_interpretation_dt, top_n = top_n, title = NULL, cex = cex)
  } else {
    layout_mat <- matrix(seq(1, cols * ceiling(num_class / cols)),
                         ncol = cols, nrow = ceiling(num_class / cols))
    par(mfcol = c(nrow(layout_mat), ncol(layout_mat)))
    for (i in seq_len(num_class)) {
      tree_interpretation_dt[, c(1, i + 1), with = FALSE] %T>%
        data.table::setnames(., old = names(.), new = c("Feature", "Contribution")) %>%
        multiple.tree.plot.interpretation(., top_n = top_n, title = paste("Class", i - 1), cex = cex)
    }
  }
}

multiple.tree.plot.interpretation <- function(tree_interpretation, top_n, title, cex) {
  tree_interpretation <- tree_interpretation[order(abs(Contribution), decreasing = TRUE),][1:min(top_n, .N),]
  if (is.null(cex)) {
    cex <- 2.5 / log2(1 + top_n)
  }
  tree_interpretation[.N:1,
                      barplot(height = Contribution, names.arg = Feature, horiz = TRUE,
                              col = ifelse(Contribution > 0, "firebrick", "steelblue"),
                              border = NA, main = title, cex.names = cex, las = 1)]
  invisible(NULL)
}
