110 lines
5.6 KiB
R
110 lines
5.6 KiB
R
#' Convert tree model dump to data.table
|
|
#'
|
|
#' Read a tree model text dump and return a data.table.
|
|
#'
|
|
#' @importFrom data.table data.table
|
|
#' @importFrom data.table set
|
|
#' @importFrom data.table rbindlist
|
|
#' @importFrom data.table :=
|
|
#' @importFrom magrittr %>%
|
|
#' @importFrom magrittr not
|
|
#' @importFrom magrittr add
|
|
#' @importFrom stringr str_extract
|
|
#' @importFrom stringr str_split
|
|
#' @importFrom stringr str_extract
|
|
#' @importFrom stringr str_trim
|
|
#' @param feature_names names of each feature as a 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 filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
|
|
#' @param n_first_tree limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.
|
|
#'
|
|
#' @return A \code{data.table} of the features used in the model with their gain, cover and few other thing.
|
|
#'
|
|
#' @details
|
|
#' General function to convert a text dump of tree model to a Matrix. The purpose is to help user to explore the model and get a better understanding of it.
|
|
#'
|
|
#' The content of the \code{data.table} is organised that way:
|
|
#'
|
|
#' \itemize{
|
|
#' \item \code{ID}: unique identifier of a node ;
|
|
#' \item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
|
|
#' \item \code{Split}: value of the chosen feature where is operated the split ;
|
|
#' \item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
|
|
#' \item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
|
|
#' \item \code{Missing}: ID of the feature for the next node in the branch for observation where the feature used for the split are not provided ;
|
|
#' \item \code{Quality}: it's the gain related to the split in this specific node ;
|
|
#' \item \code{Cover}: metric to measure the number of observation affected by the split ;
|
|
#' \item \code{Tree}: ID of the tree. It is included in the main ID ;
|
|
#' }
|
|
#'
|
|
#' @examples
|
|
#' data(agaricus.train, package='xgboost')
|
|
#'
|
|
#' #Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
|
|
#' #Each column of the sparse Matrix is a feature in one hot encoding format.
|
|
#' train <- agaricus.train
|
|
#'
|
|
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
|
|
#' eta = 1, nround = 2,objective = "binary:logistic")
|
|
#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
|
|
#'
|
|
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
|
|
#' xgb.model.dt.tree(agaricus.train$data@@Dimnames[[2]], 'xgb.model.dump')
|
|
#'
|
|
#' @export
|
|
xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, n_first_tree = NULL){
|
|
|
|
if (!class(feature_names) %in% c("character", "NULL")) {
|
|
stop("feature_names: Has to be a vector of character or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
|
|
}
|
|
if (class(filename_dump) != "character" || !file.exists(filename_dump)) {
|
|
stop("filename_dump: Has to be a path to the model dump file.")
|
|
}
|
|
if (!class(n_first_tree) %in% c("numeric", "NULL") | length(n_first_tree) > 1) {
|
|
stop("n_first_tree: Has to be a numeric vector of size 1.")
|
|
}
|
|
|
|
text <- readLines(filename_dump) %>% str_trim(side = "both")
|
|
position <- str_match(text, "booster") %>% is.na %>% not %>% which %>% c(length(text)+1)
|
|
|
|
extract <- function(x, pattern) str_extract(x, pattern) %>% str_split("=") %>% lapply(function(x) x[2] %>% as.numeric) %>% unlist
|
|
|
|
n_round <- min(length(position) - 1, n_first_tree)
|
|
|
|
addTreeId <- function(x, i) paste(i,x,sep = "-")
|
|
|
|
allTrees <- data.table()
|
|
|
|
for(i in 1:n_round){
|
|
|
|
tree <- text[(position[i]+1):(position[i+1]-1)]
|
|
|
|
notLeaf <- str_match(tree, "leaf") %>% is.na
|
|
leaf <- notLeaf %>% not %>% tree[.]
|
|
branch <- notLeaf %>% tree[.]
|
|
idBranch <- str_extract(branch, "\\d*:") %>% str_replace(":", "") %>% addTreeId(i)
|
|
idLeaf <- str_extract(leaf, "\\d*:") %>% str_replace(":", "") %>% addTreeId(i)
|
|
featureBranch <- str_extract(branch, "f\\d*<") %>% str_replace("<", "") %>% str_replace("f", "") %>% as.numeric
|
|
if(!is.null(feature_names)){
|
|
featureBranch <- feature_names[featureBranch + 1]
|
|
}
|
|
featureLeaf <- rep("Leaf", length(leaf))
|
|
splitBranch <- str_extract(branch, "<\\d*\\.*\\d*\\]") %>% str_replace("<", "") %>% str_replace("\\]", "")
|
|
splitLeaf <- rep(NA, length(leaf))
|
|
yesBranch <- extract(branch, "yes=\\d*") %>% addTreeId(i)
|
|
yesLeaf <- rep(NA, length(leaf))
|
|
noBranch <- extract(branch, "no=\\d*") %>% addTreeId(i)
|
|
noLeaf <- rep(NA, length(leaf))
|
|
missingBranch <- extract(branch, "missing=\\d+") %>% addTreeId(i)
|
|
missingLeaf <- rep(NA, length(leaf))
|
|
qualityBranch <- extract(branch, "gain=\\d*\\.*\\d*")
|
|
qualityLeaf <- extract(leaf, "leaf=\\-*\\d*\\.*\\d*")
|
|
coverBranch <- extract(branch, "cover=\\d*\\.*\\d*")
|
|
coverLeaf <- extract(leaf, "cover=\\d*\\.*\\d*")
|
|
dt <- data.table(ID = c(idBranch, idLeaf), Feature = c(featureBranch, featureLeaf), Split = c(splitBranch, splitLeaf), Yes = c(yesBranch, yesLeaf), No = c(noBranch, noLeaf), Missing = c(missingBranch, missingLeaf), Quality = c(qualityBranch, qualityLeaf), Cover = c(coverBranch, coverLeaf))[order(ID)][,Tree:=i]
|
|
|
|
allTrees <- rbindlist(list(allTrees, dt), use.names = T, fill = F)
|
|
}
|
|
|
|
allTrees
|
|
}
|