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#' Plot a boosted tree model
#'
#' Read a tree model text dump and plot the model.
#'
#' @param feature_names names of each feature as a \code{character} vector.
#' @param model produced by the \code{xgb.train} function.
#' @param trees an integer vector of tree indices that should be visualized.
#' If set to \code{NULL}, all trees of the model are included.
#' IMPORTANT: the tree index in xgboost model is zero-based
#' (e.g., use \code{trees = 0:2} for the first 3 trees in a model).
#' @param plot_width the width of the diagram in pixels.
#' @param plot_height the height of the diagram in pixels.
#' @param render a logical flag for whether the graph should be rendered (see Value).
#' @param show_node_id a logical flag for whether to show node id's in the graph.
#' @param ... currently not used.
#'
#' @details
#'
#' The content of each node is organised that way:
#'
#' \itemize{
#' \item Feature name.
#' \item \code{Cover}: The sum of second order gradient of training data classified to the leaf.
#' If it is square loss, this simply corresponds to the number of instances seen by a split
#' or collected by a leaf during training.
#' The deeper in the tree a node is, the lower this metric will be.
#' \item \code{Gain} (for split nodes): the information gain metric of a split
#' (corresponds to the importance of the node in the model).
#' \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
#' }
#' The tree root nodes also indicate the Tree index (0-based).
#'
#' The "Yes" branches are marked by the "< split_value" label.
#' The branches that also used for missing values are marked as bold
#' (as in "carrying extra capacity").
#'
#' This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
#'
#' @return
#'
#' When \code{render = TRUE}:
#' returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
#' Similar to ggplot objects, it needs to be printed to see it when not running from command line.
#'
#' When \code{render = FALSE}:
#' silently returns a graph object which is of DiagrammeR's class \code{dgr_graph}.
#' This could be useful if one wants to modify some of the graph attributes
#' before rendering the graph with \code{\link[DiagrammeR]{render_graph}}.
#'
#' @examples
#' data(agaricus.train, package='xgboost')
#'
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
#' # plot all the trees
#' xgb.plot.tree(model = bst)
#' # plot only the first tree and display the node ID:
#' xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
#'
#' \dontrun{
#' # Below is an example of how to save this plot to a file.
#' # Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
#' library(DiagrammeR)
#' gr <- xgb.plot.tree(model=bst, trees=0:1, render=FALSE)
#' export_graph(gr, 'tree.pdf', width=1500, height=1900)
#' export_graph(gr, 'tree.png', width=1500, height=1900)
#' }
#'
#' @export
xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot_width = NULL, plot_height = NULL,
render = TRUE, show_node_id = FALSE, ...){
check.deprecation(...)
if (!inherits(model, "xgb.Booster")) {
stop("model: Has to be an object of class xgb.Booster")
}
if (!requireNamespace("DiagrammeR", quietly = TRUE)) {
stop("DiagrammeR package is required for xgb.plot.tree", call. = FALSE)
}
dt <- xgb.model.dt.tree(feature_names = feature_names, model = model, trees = trees)
dt[, label:= paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Quality)]
if (show_node_id)
dt[, label := paste0(ID, ": ", label)]
dt[Node == 0, label := paste0("Tree ", Tree, "\n", label)]
dt[, shape:= "rectangle"][Feature == "Leaf", shape:= "oval"]
dt[, filledcolor:= "Beige"][Feature == "Leaf", filledcolor:= "Khaki"]
# in order to draw the first tree on top:
dt <- dt[order(-Tree)]
nodes <- DiagrammeR::create_node_df(
n = nrow(dt),
ID = dt$ID,
label = dt$label,
fillcolor = dt$filledcolor,
shape = dt$shape,
data = dt$Feature,
fontcolor = "black")
edges <- DiagrammeR::create_edge_df(
from = match(dt[Feature != "Leaf", c(ID)] %>% rep(2), dt$ID),
to = match(dt[Feature != "Leaf", c(Yes, No)], dt$ID),
label = dt[Feature != "Leaf", paste("<", Split)] %>%
c(rep("", nrow(dt[Feature != "Leaf"]))),
style = dt[Feature != "Leaf", ifelse(Missing == Yes, "bold", "solid")] %>%
c(dt[Feature != "Leaf", ifelse(Missing == No, "bold", "solid")]),
rel = "leading_to")
graph <- DiagrammeR::create_graph(
nodes_df = nodes,
edges_df = edges,
attr_theme = NULL
) %>%
DiagrammeR::add_global_graph_attrs(
attr_type = "graph",
attr = c("layout", "rankdir"),
value = c("dot", "LR")
) %>%
DiagrammeR::add_global_graph_attrs(
attr_type = "node",
attr = c("color", "style", "fontname"),
value = c("DimGray", "filled", "Helvetica")
) %>%
DiagrammeR::add_global_graph_attrs(
attr_type = "edge",
attr = c("color", "arrowsize", "arrowhead", "fontname"),
value = c("DimGray", "1.5", "vee", "Helvetica"))
if (!render) return(invisible(graph))
DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
}
# Avoid error messages during CRAN check.
# The reason is that these variables are never declared
# They are mainly column names inferred by Data.table...
globalVariables(c("Feature", "ID", "Cover", "Quality", "Split", "Yes", "No", "Missing", ".", "shape", "filledcolor", "label"))