127 lines
5.1 KiB
R
127 lines
5.1 KiB
R
#' Parse a boosted tree model text dump
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#'
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#' Parse a boosted tree model text dump into a \code{data.table} structure.
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#'
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#' @importFrom data.table data.table
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#' @importFrom data.table :=
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#' @importFrom magrittr %>%
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#' @importFrom stringr str_match
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#'
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#' @param feature_names character vector of feature names. If the model already
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#' contains feature names, this argument should be \code{NULL} (default value)
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#' @param model object of class \code{xgb.Booster}
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#' @param text \code{character} vector previously generated by the \code{xgb.dump}
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#' function (where parameter \code{with.stats = TRUE} should have been set).
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#' @param n_first_tree limit the parsing to the \code{n} first trees.
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#' If set to \code{NULL}, all trees of the model are parsed.
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#'
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#' @return
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#' A \code{data.table} with detailed information about model trees' nodes.
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#'
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#' The columns of the \code{data.table} are:
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#'
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#' \itemize{
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#' \item \code{Tree}: ID of a tree in a model
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#' \item \code{Node}: ID of a node in a tree
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#' \item \code{ID}: unique identifier of a node in a model
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#' \item \code{Feature}: for a branch node, it's a feature id or name (when available);
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#' for a leaf note, it simply labels it as \code{'Leaf'}
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#' \item \code{Split}: location of the split for a branch node (split condition is always "less than")
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#' \item \code{Yes}: ID of the next node when the split condition is met
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#' \item \code{No}: ID of the next node when the split condition is not met
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#' \item \code{Missing}: ID of the next node when branch value is missing
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#' \item \code{Quality}: either the split gain (change in loss) or the leaf value
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#' \item \code{Cover}: metric related to the number of observation either seen by a split
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#' or collected by a leaf during training.
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#' }
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#'
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#' @examples
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#' # Basic use:
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#'
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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 = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#'
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#' (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
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#'
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#'
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#' # How to match feature names of splits that are following a current 'Yes' branch:
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#'
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#' merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
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#'
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#' @export
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xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
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n_first_tree = NULL){
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if (!class(feature_names) %in% c("character", "NULL")) {
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stop("feature_names: Has to be a vector of character\n",
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" or NULL if the model dump already contains feature names.\n",
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" Look at this function documentation to see where to get feature names.")
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}
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if (class(model) != "xgb.Booster" & class(text) != "character") {
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stop("Either 'model' has to be an object of class xgb.Booster\n",
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" or 'text' has to be a character vector with the result of xgb.dump\n",
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" (or NULL if the model was provided).")
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}
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if (!class(n_first_tree) %in% c("numeric", "NULL") | length(n_first_tree) > 1) {
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stop("n_first_tree: Has to be a numeric vector of size 1.")
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}
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if(is.null(text)){
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text <- xgb.dump(model = model, with.stats = T)
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}
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position <- which(!is.na(str_match(text, "booster")))
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addTreeId <- function(x, i) paste(i,x,sep = "-")
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anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
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td <- data.table(t=text)
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td[position, Tree := 1L]
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td[, Tree := cumsum(ifelse(is.na(Tree), 0L, Tree)) - 1L]
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n_first_tree <- min(max(td$Tree), n_first_tree)
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td <- td[Tree <= n_first_tree & !grepl('^booster', t)]
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td[, Node := str_match(t, "(\\d+):")[,2] %>% as.numeric ]
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td[, ID := addTreeId(Node, Tree)]
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td[, isLeaf := !is.na(str_match(t, "leaf"))]
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# parse branch lines
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td[isLeaf==FALSE, c("Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover") := {
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rx <- paste0("f(\\d+)<(", anynumber_regex, ")\\] yes=(\\d+),no=(\\d+),missing=(\\d+),",
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"gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
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# skip some indices with spurious capture groups from anynumber_regex
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xtr <- str_match(t, rx)[, c(2,3,5,6,7,8,10)]
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xtr[, 3:5] <- addTreeId(xtr[, 3:5], Tree)
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lapply(1:ncol(xtr), function(i) xtr[,i])
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}]
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# assign feature_names when available
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td[isLeaf==FALSE & !is.null(feature_names),
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Feature := feature_names[as.numeric(Feature) + 1] ]
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# parse leaf lines
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td[isLeaf==TRUE, c("Feature", "Quality", "Cover") := {
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rx <- paste0("leaf=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
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xtr <- str_match(t, rx)[, c(2,4)]
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c("Leaf", lapply(1:ncol(xtr), function(i) xtr[,i]))
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}]
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# convert some columns to numeric
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numeric_cols <- c("Quality", "Cover")
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td[, (numeric_cols) := lapply(.SD, as.numeric), .SDcols=numeric_cols]
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td[, t := NULL]
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td[, isLeaf := NULL]
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td[order(Tree, Node)]
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}
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# Avoid error messages during CRAN check.
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# The reason is that these variables are never declared
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# They are mainly column names inferred by Data.table...
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globalVariables(c("Tree", "Node", "ID", "Feature", "t", "isLeaf",".SD", ".SDcols")) |