add new function to read model and use it in the plot function
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El Potaeto
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@ -9,6 +9,7 @@ export(xgb.cv)
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export(xgb.dump)
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export(xgb.importance)
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export(xgb.load)
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export(xgb.model.dt.tree)
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export(xgb.plot.tree)
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export(xgb.save)
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export(xgb.train)
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109
R-package/R/xgb.model.dt.tree.R
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109
R-package/R/xgb.model.dt.tree.R
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#' Convert tree model dump to data.table
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#'
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#' Read a tree model text dump and return a data.table.
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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 :=
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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_extract
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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 model dump already contains feature names, this argument should be \code{NULL}.
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#' @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}).
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#' @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.
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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 thing.
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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 Matrix. The purpose is to help user to explore the model and get a better understanding of it.
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#'
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#' The content of the \code{data.table} is organised that way:
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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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#' }
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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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#' #Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
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#' #Each column of the sparse Matrix is a feature in one hot encoding format.
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#' train <- agaricus.train
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#'
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nround = 2,objective = "binary:logistic")
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#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
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#'
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#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
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#' xgb.model.dt.tree(agaricus.train$data@@Dimnames[[2]], 'xgb.model.dump')
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#'
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#' @export
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xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, 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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}
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if (class(filename_dump) != "character" || !file.exists(filename_dump)) {
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stop("filename_dump: Has to be a path to the model dump file.")
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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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text <- readLines(filename_dump) %>% str_trim(side = "both")
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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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addTreeId <- function(x, i) paste(i,x,sep = "-")
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allTrees <- data.table()
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for(i in 1:n_round){
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tree <- text[(position[i]+1):(position[i+1]-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(i)
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idLeaf <- str_extract(leaf, "\\d*:") %>% str_replace(":", "") %>% addTreeId(i)
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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, "<\\d*\\.*\\d*\\]") %>% str_replace("<", "") %>% str_replace("\\]", "")
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splitLeaf <- rep(NA, length(leaf))
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yesBranch <- extract(branch, "yes=\\d*") %>% addTreeId(i)
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yesLeaf <- rep(NA, length(leaf))
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noBranch <- extract(branch, "no=\\d*") %>% addTreeId(i)
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noLeaf <- rep(NA, length(leaf))
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missingBranch <- extract(branch, "missing=\\d+") %>% addTreeId(i)
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missingLeaf <- rep(NA, length(leaf))
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qualityBranch <- extract(branch, "gain=\\d*\\.*\\d*")
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qualityLeaf <- extract(leaf, "leaf=\\-*\\d*\\.*\\d*")
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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:=i]
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allTrees <- rbindlist(list(allTrees, dt), use.names = T, fill = F)
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}
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allTrees
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}
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@ -1,6 +1,6 @@
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#' Plot a boosted tree model
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#'
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#' Read a xgboost model text dump.
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#' Read a tree model text dump.
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#' Plotting only works for boosted tree model (not linear model).
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#'
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#' @importFrom data.table data.table
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@ -21,7 +21,7 @@
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#' @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.
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#' @param style a \code{character} vector storing a css style to customize the appearance of nodes. Look at the \href{https://github.com/knsv/mermaid/wiki}{Mermaid wiki} for more information.
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#'
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#' @return A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
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#' @return A \code{DiagrammeR} of the model.
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#'
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#' @details
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#'
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@ -34,7 +34,7 @@
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#' }
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#'
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#' Each branch finishes with a leaf. For each leaf, only the \code{cover} is indicated.
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#' It uses Mermaid JS library for that purpose.
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#' It uses \href{https://github.com/knsv/mermaid/}{Mermaid} library for that purpose.
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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@ -53,61 +53,11 @@
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#' @export
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xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, n_first_tree = NULL, styles = 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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}
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if (class(filename_dump) != "character" || !file.exists(filename_dump)) {
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stop("filename_dump: Has to be a path to the model dump file.")
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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 (!class(styles) %in% c("character", "NULL") | length(styles) > 1) {
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stop("style: Has to be a character vector of size 1.")
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}
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text <- readLines(filename_dump) %>% str_trim(side = "both")
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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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addTreeId <- function(x, i) paste(i,x,sep = "-")
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allTrees <- data.table()
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for(i in 1:n_round){
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tree <- text[(position[i]+1):(position[i+1]-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(i)
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idLeaf <- str_extract(leaf, "\\d*:") %>% str_replace(":", "") %>% addTreeId(i)
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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, "<\\d*\\.*\\d*\\]") %>% str_replace("<", "") %>% str_replace("\\]", "")
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splitLeaf <- rep(NA, length(leaf))
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yesBranch <- extract(branch, "yes=\\d*") %>% addTreeId(i)
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yesLeaf <- rep(NA, length(leaf))
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noBranch <- extract(branch, "no=\\d*") %>% addTreeId(i)
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noLeaf <- rep(NA, length(leaf))
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missingBranch <- extract(branch, "missing=\\d+") %>% addTreeId(i)
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missingLeaf <- rep(NA, length(leaf))
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qualityBranch <- extract(branch, "gain=\\d*\\.*\\d*")
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qualityLeaf <- extract(leaf, "leaf=\\-*\\d*\\.*\\d*")
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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:=i]
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allTrees <- rbindlist(list(allTrees, dt), use.names = T, fill = F)
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}
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allTrees <- xgb.model.dt.tree(feature_names, filename_dump, n_first_tree)
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set(allTrees, i = which(allTrees[,Feature]!= "Leaf"), j = "YesFeature", value = merge(copy(allTrees)[,ID:=Yes][, .(ID)], allTrees[,.(ID, Feature, Quality, Cover)], by = "ID")[,paste(Feature, "<br/>Cover: ", Cover, sep = "")])
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54
R-package/man/xgb.model.dt.tree.Rd
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54
R-package/man/xgb.model.dt.tree.Rd
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@ -0,0 +1,54 @@
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% Generated by roxygen2 (4.1.0): do not edit by hand
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% Please edit documentation in R/xgb.model.dt.tree.R
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\name{xgb.model.dt.tree}
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\alias{xgb.model.dt.tree}
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\title{Convert tree model dump to data.table}
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\usage{
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xgb.model.dt.tree(feature_names = NULL, filename_dump = 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 model dump already contains feature names, this argument should be \code{NULL}.}
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\item{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}).}
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\item{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.}
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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 thing.
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}
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\description{
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Read a tree model text dump and return a 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 Matrix. The purpose is to help user to explore the model and get a better understanding of it.
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The content of the \code{data.table} is organised that way:
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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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}
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}
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\examples{
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data(agaricus.train, package='xgboost')
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#Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
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#Each column of the sparse Matrix is a feature in one hot encoding format.
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train <- agaricus.train
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nround = 2,objective = "binary:logistic")
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xgb.dump(bst, 'xgb.model.dump', with.stats = T)
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#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
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xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], 'xgb.model.dump')
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}
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@ -17,14 +17,13 @@ xgb.plot.tree(feature_names = NULL, filename_dump = NULL,
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\item{style}{a \code{character} vector storing a css style to customize the appearance of nodes. Look at the \href{https://github.com/knsv/mermaid/wiki}{Mermaid wiki} for more information.}
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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 average gain (and their weight for boosted tree model) in the model.
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A \code{DiagrammeR} of the model.
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}
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\description{
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Read a xgboost model text dump.
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Read a tree model text dump.
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Plotting only works for boosted tree model (not linear model).
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}
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\details{
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Plotting only works for boosted tree model (not linear model).
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The content of each node is organised that way:
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\itemize{
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@ -34,7 +33,7 @@ The content of each node is organised that way:
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}
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Each branch finishes with a leaf. For each leaf, only the \code{cover} is indicated.
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It uses Mermaid JS library for that purpose.
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It uses \href{https://github.com/knsv/mermaid/}{Mermaid} library for that purpose.
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}
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\examples{
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data(agaricus.train, package='xgboost')
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