#' Project all trees on one tree and plot it
#' 
#' Visualization of the ensemble of trees as a single collective unit.
#'
#' @importFrom data.table data.table
#' @importFrom data.table rbindlist
#' @importFrom data.table setnames
#' @importFrom data.table :=
#' @importFrom magrittr %>%
#' @importFrom stringr str_detect
#' @importFrom stringr str_extract
#' 
#' @param model dump generated by the \code{xgb.train} function.
#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param features.keep number of features to keep in each position of the multi trees.
#' @param plot.width width in pixels of the graph to produce
#' @param plot.height height in pixels of the graph to produce
#' 
#' @return Two graphs showing the distribution of the model deepness.
#' 
#' @details
#' 
#' This function tries to capture the complexity of gradient boosted tree ensemble 
#' in a cohesive way. 
#' 
#' The goal is to improve the interpretability of the model generally seen as black box.
#' The function is dedicated to boosting applied to decision trees only.
#' 
#' The purpose is to move from an ensemble of trees to a single tree only.
#' 
#' It takes advantage of the fact that the shape of a binary tree is only defined by 
#' its deepness (therefore in a boosting model, all trees have the same shape). 
#' 
#' Moreover, the trees tend to reuse the same features.
#' 
#' The function will project each tree on one, and keep for each position the 
#' \code{features.keep} first features (based on Gain per feature measure).
#' 
#' This function is inspired by this blog post:
#' \url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
#'
#' @examples
#' data(agaricus.train, package='xgboost')
#'
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
#'                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
#'                  min_child_weight = 50)
#'
#' p <- xgb.plot.multi.trees(model = bst, feature_names = agaricus.train$data@Dimnames[[2]], features.keep = 3)
#' print(p)
#'
#' @export
xgb.plot.multi.trees <- function(model, feature_names = NULL, features.keep = 5, plot.width = NULL, plot.height = NULL){
  tree.matrix <- xgb.model.dt.tree(feature_names = feature_names, model = model)
  
  # first number of the path represents the tree, then the following numbers are related to the path to follow
  # root init
  root.nodes <- tree.matrix[str_detect(ID, "\\d+-0"), ID]
  tree.matrix[ID %in% root.nodes, abs.node.position:=root.nodes]
  
  precedent.nodes <- root.nodes
  
  while(tree.matrix[,sum(is.na(abs.node.position))] > 0) {
    yes.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(Yes)]
    no.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(No)]
    yes.nodes.abs.pos <- yes.row.nodes[, abs.node.position] %>% paste0("_0")
    no.nodes.abs.pos <- no.row.nodes[, abs.node.position] %>% paste0("_1")
    
    tree.matrix[ID %in% yes.row.nodes[, Yes], abs.node.position := yes.nodes.abs.pos]
    tree.matrix[ID %in% no.row.nodes[, No], abs.node.position := no.nodes.abs.pos]
    precedent.nodes <- c(yes.nodes.abs.pos, no.nodes.abs.pos)
  }
  
  tree.matrix[!is.na(Yes),Yes:= paste0(abs.node.position, "_0")]
  tree.matrix[!is.na(No),No:= paste0(abs.node.position, "_1")]
  
  
  
  remove.tree <- . %>% str_replace(pattern = "^\\d+-", replacement = "")
  
  tree.matrix[,`:=`(abs.node.position=remove.tree(abs.node.position), Yes=remove.tree(Yes), No=remove.tree(No))]
  
  nodes.dt <- tree.matrix[,.(Quality = sum(Quality)),by = .(abs.node.position, Feature)][,.(Text =paste0(Feature[1:min(length(Feature), features.keep)], " (", Quality[1:min(length(Quality), features.keep)], ")") %>% paste0(collapse = "\n")), by=abs.node.position]
  edges.dt <- tree.matrix[Feature != "Leaf",.(abs.node.position, Yes)] %>% list(tree.matrix[Feature != "Leaf",.(abs.node.position, No)]) %>% rbindlist() %>% setnames(c("From", "To")) %>% .[,.N,.(From, To)] %>% .[,N:=NULL]
  
  nodes <- DiagrammeR::create_nodes(nodes = nodes.dt[,abs.node.position],
                                    label = nodes.dt[,Text],
                                    style = "filled",
                                    color = "DimGray",
                                    fillcolor= "Beige",
                                    shape = "oval",
                                    fontname = "Helvetica"
  )
  
  edges <- DiagrammeR::create_edges(from = edges.dt[,From],
                                    to = edges.dt[,To],
                                    color = "DimGray", 
                                    arrowsize = "1.5", 
                                    arrowhead = "vee",
                                    fontname = "Helvetica",
                                    rel = "leading_to")
  
  graph <- DiagrammeR::create_graph(nodes_df = nodes,
                                    edges_df = edges,
                                    graph_attrs = "rankdir = LR")
  
  DiagrammeR::render_graph(graph, width = plot.width, height = plot.height)  
}

globalVariables(
  c(
    "Feature", "no.nodes.abs.pos", "ID", "Yes", "No", "Tree", "yes.nodes.abs.pos", "abs.node.position"
  )
)