109 lines
5.2 KiB
R
109 lines
5.2 KiB
R
#' Project all trees on one tree and plot it
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#'
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#' Visualization of the ensemble of trees as a single collective unit.
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#'
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#' @param model dump generated by the \code{xgb.train} function.
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#' @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}.
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#' @param features_keep number of features to keep in each position of the multi trees.
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#' @param plot_width width in pixels of the graph to produce
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#' @param plot_height height in pixels of the graph to produce
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#' @param ... currently not used
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#'
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#' @return Two graphs showing the distribution of the model deepness.
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#'
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#' @details
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#'
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#' This function tries to capture the complexity of gradient boosted tree ensemble
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#' in a cohesive way.
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#'
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#' The goal is to improve the interpretability of the model generally seen as black box.
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#' The function is dedicated to boosting applied to decision trees only.
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#'
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#' The purpose is to move from an ensemble of trees to a single tree only.
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#'
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#' It takes advantage of the fact that the shape of a binary tree is only defined by
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#' its deepness (therefore in a boosting model, all trees have the same shape).
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#'
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#' Moreover, the trees tend to reuse the same features.
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#'
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#' The function will project each tree on one, and keep for each position the
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#' \code{features_keep} first features (based on Gain per feature measure).
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#'
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#' This function is inspired by this blog post:
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#' \url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
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#' eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
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#' min_child_weight = 50)
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#'
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#' p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data),
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#' features_keep = 3)
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#' print(p)
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#'
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#' @export
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xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5, plot_width = NULL, plot_height = NULL, ...){
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check.deprecation(...)
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tree.matrix <- xgb.model.dt.tree(feature_names = feature_names, model = model)
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# first number of the path represents the tree, then the following numbers are related to the path to follow
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# root init
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root.nodes <- tree.matrix[stri_detect_regex(ID, "\\d+-0"), ID]
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tree.matrix[ID %in% root.nodes, abs.node.position:=root.nodes]
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precedent.nodes <- root.nodes
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while(tree.matrix[,sum(is.na(abs.node.position))] > 0) {
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yes.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(Yes)]
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no.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(No)]
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yes.nodes.abs.pos <- yes.row.nodes[, abs.node.position] %>% paste0("_0")
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no.nodes.abs.pos <- no.row.nodes[, abs.node.position] %>% paste0("_1")
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tree.matrix[ID %in% yes.row.nodes[, Yes], abs.node.position := yes.nodes.abs.pos]
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tree.matrix[ID %in% no.row.nodes[, No], abs.node.position := no.nodes.abs.pos]
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precedent.nodes <- c(yes.nodes.abs.pos, no.nodes.abs.pos)
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}
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tree.matrix[!is.na(Yes),Yes:= paste0(abs.node.position, "_0")]
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tree.matrix[!is.na(No),No:= paste0(abs.node.position, "_1")]
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remove.tree <- . %>% stri_replace_first_regex(pattern = "^\\d+-", replacement = "")
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tree.matrix[,`:=`(abs.node.position=remove.tree(abs.node.position), Yes=remove.tree(Yes), No=remove.tree(No))]
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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]
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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]
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nodes <- DiagrammeR::create_node_df(n = nrow(nodes.dt),
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label = nodes.dt[,Text],
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style = "filled",
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color = "DimGray",
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fillcolor= "Beige",
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shape = "oval",
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fontname = "Helvetica"
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)
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edges <- DiagrammeR::create_edge_df(from = match(edges.dt[,From], nodes.dt[,abs.node.position]),
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to = match(edges.dt[,To], nodes.dt[,abs.node.position]),
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color = "DimGray",
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arrowsize = "1.5",
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arrowhead = "vee",
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fontname = "Helvetica",
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rel = "leading_to")
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graph <- DiagrammeR::create_graph(nodes_df = nodes,
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edges_df = edges)
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DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
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}
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globalVariables(
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c(
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".N", "N", "From", "To", "Text", "Feature", "no.nodes.abs.pos", "ID", "Yes", "No", "Tree", "yes.nodes.abs.pos", "abs.node.position"
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)
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)
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