new documentation

new import

R-package/NAMESPACE

@@ -9,6 +9,7 @@ export(xgb.cv)
 export(xgb.dump)
 export(xgb.importance)
 export(xgb.load)
+export(xgb.plot.tree)
 export(xgb.save)
 export(xgb.train)
 export(xgboost)
@@ -16,13 +17,16 @@ exportMethods(predict)
 import(methods)
 importClassesFrom(Matrix,dgCMatrix)
 importClassesFrom(Matrix,dgeMatrix)
+importFrom(DiagrammeR,DiagrammeR)
 importFrom(data.table,":=")
 importFrom(data.table,as.data.table)
 importFrom(data.table,data.table)
 importFrom(data.table,rbindlist)
+importFrom(data.table,set)
 importFrom(magrittr,"%>%")
 importFrom(stringr,str_extract)
 importFrom(stringr,str_extract_all)
 importFrom(stringr,str_match)
 importFrom(stringr,str_replace)
 importFrom(stringr,str_split)
+importFrom(stringr,str_trim)

R-package/man/xgb.plot.tree.Rd

@@ -0,0 +1,40 @@
+% Generated by roxygen2 (4.1.0): do not edit by hand
+% Please edit documentation in R/xgb.plot.tree.R
+\name{xgb.plot.tree}
+\alias{xgb.plot.tree}
+\title{Plot a boosted tree model}
+\usage{
+xgb.plot.tree(feature_names = NULL, filename_dump = NULL)
+}
+\arguments{
+\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}.}
+
+\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).}
+}
+\value{
+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.
+}
+\description{
+Read a xgboost model text dump.
+Only works for boosted tree model (not linear model).
+}
+\details{
+This is the function to plot the trees growned.
+It uses Mermaid JS library for that purpose.
+Performance can be low for huge models.
+}
+\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$data@Dimnames[[2]] represents the column names of the sparse matrix.
+xgb.plot.tree(agaricus.train$data@Dimnames[[2]], 'xgb.model.dump')
+}
+