a375ad2822
* [R] make sure things work for a single split model; fixes #2191 * [R] add option use_int_id to xgb.model.dt.tree * [R] add example of exporting tree plot to a file * [R] set save_period = NULL as default in xgboost() to be the same as in xgb.train; fixes #2182 * [R] it's a good practice after CRAN releases to bump up package version in dev * [R] allow xgb.DMatrix construction from integer dense matrices * [R] xgb.DMatrix: silent parameter; improve documentation * [R] xgb.model.dt.tree code style changes * [R] update NEWS with parameter changes * [R] code safety & style; handle non-strict matrix and inherited classes of input and model; fixes #2242 * [R] change to x.y.z.p R-package versioning scheme and set version to 0.6.4.3 * [R] add an R package versioning section to the contributors guide * [R] R-package/README.md: clean up the redundant old installation instructions, link the contributors guide
160 lines
6.9 KiB
R
160 lines
6.9 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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#' @param feature_names character vector of feature names. If the model already
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#' contains feature names, those would be used when \code{feature_names=NULL} (default value).
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#' Non-null \code{feature_names} could be provided to override those in the model.
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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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#' \code{text} takes precedence over \code{model}.
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#' @param trees an integer vector of tree indices that should be parsed.
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#' If set to \code{NULL}, all trees of the model are parsed.
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#' It could be useful, e.g., in multiclass classification to get only
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#' the trees of one certain class. IMPORTANT: the tree index in xgboost models
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#' is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).
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#' @param use_int_id a logical flag indicating whether nodes in columns "Yes", "No", "Missing" should be
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#' represented as integers (when FALSE) or as "Tree-Node" character strings (when FALSE).
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#' @param ... currently not used.
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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}: integer ID of a tree in a model (zero-based index)
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#' \item \code{Node}: integer ID of a node in a tree (zero-based index)
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#' \item \code{ID}: character identifier of a node in a model (only when \code{use_int_id=FALSE})
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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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#' When \code{use_int_id=FALSE}, columns "Yes", "No", and "Missing" point to model-wide node identifiers
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#' in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from
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#' the corresponding trees in the "Node" column.
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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, nrounds = 2,objective = "binary:logistic")
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#'
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#' (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
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#'
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#' # This bst model already has feature_names stored with it, so those would be used when
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#' # feature_names is not set:
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#' (dt <- xgb.model.dt.tree(model = bst))
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#'
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#' # How to match feature names of splits that are following a current 'Yes' branch:
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#'
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#' merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
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#'
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#' @export
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xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
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trees = NULL, use_int_id = FALSE, ...){
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check.deprecation(...)
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if (!inherits(model, "xgb.Booster") & !is.character(text)) {
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stop("Either 'model' must be an object of class xgb.Booster\n",
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" or 'text' must be a character vector with the result of xgb.dump\n",
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" (or NULL if 'model' was provided).")
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}
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if (is.null(feature_names) && !is.null(model) && !is.null(model$feature_names))
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feature_names <- model$feature_names
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if (!(is.null(feature_names) || is.character(feature_names))) {
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stop("feature_names: must be a character vector")
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}
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if (!(is.null(trees) || is.numeric(trees))) {
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stop("trees: must be a vector of integers.")
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}
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if (is.null(text)){
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text <- xgb.dump(model = model, with_stats = TRUE)
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}
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if (length(text) < 2 ||
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sum(stri_detect_regex(text, 'yes=(\\d+),no=(\\d+)')) < 1) {
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stop("Non-tree model detected! This function can only be used with tree models.")
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}
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position <- which(!is.na(stri_match_first_regex(text, "booster")))
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add.tree.id <- function(node, tree) if (use_int_id) node else paste(tree, node, sep = "-")
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anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
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td <- data.table(t = text)
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td[position, Tree := 1L]
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td[, Tree := cumsum(ifelse(is.na(Tree), 0L, Tree)) - 1L]
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if (is.null(trees)) {
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trees <- 0:max(td$Tree)
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} else {
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trees <- trees[trees >= 0 & trees <= max(td$Tree)]
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}
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td <- td[Tree %in% trees & !grepl('^booster', t)]
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td[, Node := stri_match_first_regex(t, "(\\d+):")[,2] %>% as.integer ]
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if (!use_int_id) td[, ID := add.tree.id(Node, Tree)]
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td[, isLeaf := !is.na(stri_match_first_regex(t, "leaf"))]
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# parse branch lines
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branch_rx <- paste0("f(\\d+)<(", anynumber_regex, ")\\] yes=(\\d+),no=(\\d+),missing=(\\d+),",
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"gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
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branch_cols <- c("Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
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td[isLeaf == FALSE,
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(branch_cols) := {
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# skip some indices with spurious capture groups from anynumber_regex
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xtr <- stri_match_first_regex(t, branch_rx)[, c(2,3,5,6,7,8,10), drop = FALSE]
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xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
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lapply(1:ncol(xtr), function(i) xtr[,i])
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}]
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# assign feature_names when available
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if (!is.null(feature_names)) {
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if (length(feature_names) <= max(as.numeric(td$Feature), na.rm = TRUE))
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stop("feature_names has less elements than there are features used in the model")
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td[isLeaf == FALSE, Feature := feature_names[as.numeric(Feature) + 1] ]
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}
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# parse leaf lines
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leaf_rx <- paste0("leaf=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
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leaf_cols <- c("Feature", "Quality", "Cover")
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td[isLeaf == TRUE,
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(leaf_cols) := {
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xtr <- stri_match_first_regex(t, leaf_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("Split", "Quality", "Cover")
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td[, (numeric_cols) := lapply(.SD, as.numeric), .SDcols = numeric_cols]
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if (use_int_id) {
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int_cols <- c("Yes", "No", "Missing")
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td[, (int_cols) := lapply(.SD, as.integer), .SDcols = int_cols]
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}
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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"))
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