add new function to read model and use it in the plot function

R-package/man/xgb.model.dt.tree.Rd

@@ -0,0 +1,54 @@
+% Generated by roxygen2 (4.1.0): do not edit by hand
+% Please edit documentation in R/xgb.model.dt.tree.R
+\name{xgb.model.dt.tree}
+\alias{xgb.model.dt.tree}
+\title{Convert tree model dump to data.table}
+\usage{
+xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL,
+  n_first_tree = 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 (parameter \code{with.stats = T} in function \code{xgb.dump}).}
+
+\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.}
+}
+\value{
+A \code{data.table} of the features used in the model with their gain, cover and few other thing.
+}
+\description{
+Read a tree model text dump and return a data.table.
+}
+\details{
+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.
+
+The content of the \code{data.table} is organised that way:
+
+\itemize{
+\item \code{ID}: unique identifier of a node ;
+ \item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
+ \item \code{Split}: value of the chosen feature where is operated the split ;
+ \item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
+ \item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
+ \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 ;
+ \item \code{Quality}: it's the gain related to the split in this specific node ;
+ \item \code{Cover}: metric to measure the number of observation affected by the split ;
+ \item \code{Tree}: ID of the tree. It is included in the main ID ;
+}
+}
+\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$data@Dimnames[[2]] represents the column names of the sparse matrix.
+xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], 'xgb.model.dump')
+}
+

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

@@ -17,14 +17,13 @@ xgb.plot.tree(feature_names = NULL, filename_dump = NULL,
 \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.}
 }
 \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.
+A \code{DiagrammeR} of the model.
 }
 \description{
-Read a xgboost model text dump.
+Read a tree model text dump.
+Plotting only works for boosted tree model (not linear model).
 }
 \details{
-Plotting only works for boosted tree model (not linear model).
-
 The content of each node is organised that way:
 
 \itemize{
@@ -34,7 +33,7 @@ The content of each node is organised that way:
 }
 
 Each branch finishes with a leaf. For each leaf, only the \code{cover} is indicated.
-It uses Mermaid JS library for that purpose.
+It uses \href{https://github.com/knsv/mermaid/}{Mermaid} library for that purpose.
 }
 \examples{
 data(agaricus.train, package='xgboost')