xgb.model.dt.tree up to x100 faster
This commit is contained in:
Vadim Khotilovich
parent
49bbd72d08
commit
be65949ba2
@ -43,19 +43,14 @@ importFrom(Matrix,sparse.model.matrix)
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importFrom(Matrix,sparseVector)
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importFrom(data.table,":=")
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importFrom(data.table,as.data.table)
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importFrom(data.table,copy)
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importFrom(data.table,data.table)
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importFrom(data.table,fread)
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importFrom(data.table,rbindlist)
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importFrom(data.table,set)
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importFrom(data.table,setnames)
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importFrom(magrittr,"%>%")
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importFrom(magrittr,add)
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importFrom(magrittr,not)
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importFrom(stringr,str_detect)
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importFrom(stringr,str_extract)
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importFrom(stringr,str_extract_all)
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importFrom(stringr,str_match)
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importFrom(stringr,str_replace)
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importFrom(stringr,str_split)
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importFrom(stringr,str_trim)
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@ -1,153 +1,106 @@
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#' Parse boosted tree model text dump
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#'
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#' Parse a boosted tree model text dump and return a \code{data.table}.
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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 set
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#' @importFrom data.table rbindlist
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#' @importFrom data.table copy
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#' @importFrom data.table :=
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#' @importFrom magrittr %>%
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#' @importFrom magrittr not
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#' @importFrom magrittr add
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#' @importFrom stringr str_extract
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#' @importFrom stringr str_split
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#' @importFrom stringr str_trim
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#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If the model already contains feature names, this argument should be \code{NULL} (default value).
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#' @param model object created by the \code{xgb.train} function.
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#' @param text \code{character} vector generated by the \code{xgb.dump} function. Model dump must include the gain per feature and per tree (parameter \code{with.stats = TRUE} in function \code{xgb.dump}).
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#' @param n_first_tree limit the plot to the \code{n} first trees. If set to \code{NULL}, all trees of the model are plotted. Performance can be low depending of the size of the model.
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#'
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#' @return A \code{data.table} of the features used in the model with their gain, cover and few other information.
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#'
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#' @details
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#' General function to convert a text dump of tree model to a \code{data.table}.
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#'
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#' The purpose is to help user to explore the model and get a better understanding of it.
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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{ID}: unique identifier of a node ;
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#' \item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
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#' \item \code{Split}: value of the chosen feature where is operated the split ;
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#' \item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
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#' \item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
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#' \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 ;
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#' \item \code{Quality}: it's the gain related to the split in this specific node ;
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#' \item \code{Cover}: metric to measure the number of observation affected by the split ;
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#' \item \code{Tree}: ID of the tree. It is included in the main ID ;
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#' \item \code{Yes.Feature}, \code{No.Feature}, \code{Yes.Cover}, \code{No.Cover}, \code{Yes.Quality} and \code{No.Quality}: data related to the pointer in \code{Yes} or \code{No} column ;
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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 or the leaf value
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#' \item \code{Cover}: metric related to the number of observation either seen by a split split
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#' or collected by a leaf during training.
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#' }
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#'
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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 = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#'
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#' # agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
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#' xgb.model.dt.tree(feature_names = agaricus.train$data@@Dimnames[[2]], model = bst)
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#' xgb.model.dt.tree(colnames(agaricus.train$data), bst)
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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, n_first_tree = NULL){
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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 or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
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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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"model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.\n" %>%
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paste0("text: Has to be a vector of character or NULL if a path to the model dump has already been provided.") %>%
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stop()
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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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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 <- str_match(text, "booster") %>% is.na %>% not %>% which %>% c(length(text) + 1)
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extract <- function(x, pattern) str_extract(x, pattern) %>% str_split("=") %>% lapply(function(x) x[2] %>% as.numeric) %>% unlist
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n_round <- min(length(position) - 1, n_first_tree)
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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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allTrees <- data.table()
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anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
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for (i in 1:n_round){
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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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td[isLeaf==TRUE, Feature := "Leaf"]
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td[isLeaf==FALSE, Feature := str_match(t, "f(\\d+)<")[,2] ]
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td[isLeaf==FALSE & !is.null(feature_names), Feature := feature_names[as.numeric(Feature) + 1] ]
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td[isLeaf==FALSE, Split := str_match(t, paste0("<(",anynumber_regex,")\\]"))[,2] ]
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td[isLeaf==FALSE, Yes := str_match(t, "yes=(\\d+)")[,2] %>% addTreeId(Tree) ]
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td[isLeaf==FALSE, No := str_match(t, "no=(\\d+)")[,2] %>% addTreeId(Tree) ]
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td[isLeaf==FALSE, Missing := str_match(t, "missing=(\\d+)")[,2] %>% addTreeId(Tree) ]
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td[isLeaf==FALSE, Quality := str_match(t, paste0("gain=(",anynumber_regex,")"))[,2] %>% as.numeric ]
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td[isLeaf==TRUE, Quality := str_match(t, paste0("leaf=(",anynumber_regex,")"))[,2] %>% as.numeric ]
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td[, Cover := str_match(t, paste0("cover=(\\d*\\.*\\d*)"))[,2] %>% as.numeric ]
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td[, t := NULL]
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td[, isLeaf := NULL]
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tree <- text[(position[i] + 1):(position[i + 1] - 1)]
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# avoid tree made of a leaf only (no split)
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if(length(tree) < 2) next
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treeID <- i - 1
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notLeaf <- str_match(tree, "leaf") %>% is.na
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leaf <- notLeaf %>% not %>% tree[.]
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branch <- notLeaf %>% tree[.]
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idBranch <- str_extract(branch, "\\d*:") %>% str_replace(":", "") %>% addTreeId(treeID)
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idLeaf <- str_extract(leaf, "\\d*:") %>% str_replace(":", "") %>% addTreeId(treeID)
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featureBranch <- str_extract(branch, "f\\d*<") %>% str_replace("<", "") %>% str_replace("f", "") %>% as.numeric
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if(!is.null(feature_names)){
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featureBranch <- feature_names[featureBranch + 1]
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}
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featureLeaf <- rep("Leaf", length(leaf))
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splitBranch <- str_extract(branch, paste0("<",anynumber_regex,"\\]")) %>% str_replace("<", "") %>% str_replace("\\]", "")
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splitLeaf <- rep(NA, length(leaf))
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yesBranch <- extract(branch, "yes=\\d*") %>% addTreeId(treeID)
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yesLeaf <- rep(NA, length(leaf))
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noBranch <- extract(branch, "no=\\d*") %>% addTreeId(treeID)
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noLeaf <- rep(NA, length(leaf))
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missingBranch <- extract(branch, "missing=\\d+") %>% addTreeId(treeID)
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missingLeaf <- rep(NA, length(leaf))
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qualityBranch <- extract(branch, paste0("gain=",anynumber_regex))
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qualityLeaf <- extract(leaf, paste0("leaf=",anynumber_regex))
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coverBranch <- extract(branch, "cover=\\d*\\.*\\d*")
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coverLeaf <- extract(leaf, "cover=\\d*\\.*\\d*")
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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 := treeID]
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allTrees <- rbindlist(list(allTrees, dt), use.names = T, fill = F)
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}
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yes <- allTrees[!is.na(Yes), Yes]
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set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
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j = "Yes.Feature",
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value = allTrees[ID %in% yes, Feature])
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set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
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j = "Yes.Cover",
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value = allTrees[ID %in% yes, Cover])
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set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
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j = "Yes.Quality",
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value = allTrees[ID %in% yes, Quality])
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no <- allTrees[!is.na(No), No]
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set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
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j = "No.Feature",
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value = allTrees[ID %in% no, Feature])
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set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
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j = "No.Cover",
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value = allTrees[ID %in% no, Cover])
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set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
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j = "No.Quality",
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value = allTrees[ID %in% no, Quality])
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allTrees
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td
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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("ID", "Tree", "Yes", ".", ".N", "Feature", "Cover", "Quality", "No", "Gain", "Frequency"))
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globalVariables(c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover"))
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@ -8,48 +8,47 @@ xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
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n_first_tree = NULL)
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}
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\arguments{
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\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If the model already contains feature names, this argument should be \code{NULL} (default value).}
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\item{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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\item{model}{object created by the \code{xgb.train} function.}
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\item{model}{object of class \code{xgb.Booster}}
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\item{text}{\code{character} vector generated by the \code{xgb.dump} function. Model dump must include the gain per feature and per tree (parameter \code{with.stats = TRUE} in function \code{xgb.dump}).}
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\item{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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\item{n_first_tree}{limit the plot to the \code{n} first trees. If set to \code{NULL}, all trees of the model are plotted. Performance can be low depending of the size of the model.}
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\item{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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\value{
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A \code{data.table} of the features used in the model with their gain, cover and few other information.
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}
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\description{
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Parse a boosted tree model text dump and return a \code{data.table}.
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}
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\details{
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General function to convert a text dump of tree model to a \code{data.table}.
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The purpose is to help user to explore the model and get a better understanding of it.
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A \code{data.table} with detailed information about model trees' nodes.
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The columns of the \code{data.table} are:
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\itemize{
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\item \code{ID}: unique identifier of a node ;
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\item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
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\item \code{Split}: value of the chosen feature where is operated the split ;
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\item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
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\item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
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\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 ;
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\item \code{Quality}: it's the gain related to the split in this specific node ;
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\item \code{Cover}: metric to measure the number of observation affected by the split ;
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\item \code{Tree}: ID of the tree. It is included in the main ID ;
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\item \code{Yes.Feature}, \code{No.Feature}, \code{Yes.Cover}, \code{No.Cover}, \code{Yes.Quality} and \code{No.Quality}: data related to the pointer in \code{Yes} or \code{No} column ;
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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 or the leaf value
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\item \code{Cover}: metric related to the number of observation either seen by a split split
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or collected by a leaf during training.
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}
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}
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\description{
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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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\examples{
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data(agaricus.train, package='xgboost')
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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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# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
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xgb.model.dt.tree(feature_names = agaricus.train$data@Dimnames[[2]], model = bst)
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xgb.model.dt.tree(colnames(agaricus.train$data), bst)
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}
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@ -20,7 +20,7 @@ bst.Tree <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
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bst.GLM <- xgboost(data = sparse_matrix, label = output_vector,
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eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gblinear")
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feature.names <- agaricus.train$data@Dimnames[[2]]
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feature.names <- colnames(agaricus.train$data)
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test_that("xgb.dump works", {
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capture.output(print(xgb.dump(bst.Tree)))
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@ -57,11 +57,10 @@ test_that("xgb-attribute functionality", {
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})
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test_that("xgb.model.dt.tree works with and without feature names", {
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names.dt.trees <- c("ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover",
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"Tree", "Yes.Feature", "Yes.Cover", "Yes.Quality", "No.Feature", "No.Cover", "No.Quality")
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names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
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dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
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expect_equal(names.dt.trees, names(dt.tree))
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expect_equal(dim(dt.tree), c(162, 15))
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expect_equal(dim(dt.tree), c(162, 10))
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xgb.model.dt.tree(model = bst.Tree)
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})
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