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New documentation rewording

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This commit is contained in:
Michaël Benesty 2015-12-09 18:26:56 +01:00
parent f761432c11
commit b2e68b8dc7
6 changed files with 20 additions and 32 deletions
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4
R-package/R/xgb.plot.deepness.R
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@ -69,7 +69,7 @@ get.paths.to.leaf <- function(dt.tree) {
#' @importFrom data.table setnames
#' @importFrom data.table :=
#' @importFrom magrittr %>%
#' @param model dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#' @param model dump generated by the \code{xgb.train} function.
#'
#' @return Two graphs showing the distribution of the model deepness.
#'
@ -86,7 +86,7 @@ get.paths.to.leaf <- function(dt.tree) {
#'
#' \itemize{
#' \item Count: number of leaf per level of deepness;
#' \item Weighted cover: noramlized weighted cover per Leaf (weighted number of instances).
#' \item Weighted cover: noramlized weighted cover per leaf (weighted number of instances).
#' }
#'
#' This function is inspired by the blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}

4
R-package/R/xgb.plot.multi.trees.R
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@ -10,8 +10,8 @@
#' @importFrom stringr str_detect
#' @importFrom stringr str_extract
#'
#' @param model dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#' @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}.
#' @param model dump generated by the \code{xgb.train} function.
#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param features.keep number of features to keep in each position of the multi trees.
#' @param plot.width width in pixels of the graph to produce
#' @param plot.height height in pixels of the graph to produce

14
R-package/R/xgb.plot.tree.R
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@ -1,12 +1,11 @@
#' Plot a boosted tree model
#'
#' Read a tree model text dump.
#' Plotting only works for boosted tree model (not linear model).
#' Read a tree model text dump and plot the model.
#'
#' @importFrom data.table data.table
#' @importFrom data.table :=
#' @importFrom magrittr %>%
#' @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}.
#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#' @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.
#' @param plot.width the width of the diagram in pixels.
@ -24,20 +23,15 @@
#' \item \code{gain}: metric the importance of the node in the model.
#' }
#'
#' Each branch finishes with a leaf. For each leaf, only the \code{cover} is indicated.
#' It uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
#' The function uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
#'
#' @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.
#'
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#'
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' # agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.plot.tree(feature_names = agaricus.train$data@@Dimnames[[2]], model = bst)
#'
#' @export

4
R-package/man/xgb.plot.deepness.Rd
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@ -7,7 +7,7 @@
xgb.plot.deepness(model = NULL)
}
\arguments{
\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
\item{model}{dump generated by the \code{xgb.train} function.}
}
\value{
Two graphs showing the distribution of the model deepness.
@ -28,7 +28,7 @@ The graph is made of two parts:
\itemize{
\item Count: number of leaf per level of deepness;
\item Weighted cover: noramlized weighted cover per Leaf (weighted number of instances).
\item Weighted cover: noramlized weighted cover per leaf (weighted number of instances).
}
This function is inspired by the blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}

4
R-package/man/xgb.plot.multi.trees.Rd
View File

@ -8,9 +8,9 @@ xgb.plot.multi.trees(model, feature_names = NULL, features.keep = 5,
plot.width = NULL, plot.height = NULL)
}
\arguments{
\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
\item{model}{dump generated by the \code{xgb.train} function.}
\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{feature_names}{names of each feature as a \code{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{features.keep}{number of features to keep in each position of the multi trees.}

14
R-package/man/xgb.plot.tree.Rd
View File

@ -8,7 +8,7 @@ xgb.plot.tree(feature_names = NULL, model = NULL, n_first_tree = NULL,
plot.width = NULL, plot.height = 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{feature_names}{names of each feature as a \code{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{model}{generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
@ -22,8 +22,7 @@ xgb.plot.tree(feature_names = NULL, model = NULL, n_first_tree = NULL,
A \code{DiagrammeR} of the model.
}
\description{
Read a tree model text dump.
Plotting only works for boosted tree model (not linear model).
Read a tree model text dump and plot the model.
}
\details{
The content of each node is organised that way:
@ -34,20 +33,15 @@ The content of each node is organised that way:
\item \code{gain}: metric the importance of the node in the model.
}
Each branch finishes with a leaf. For each leaf, only the \code{cover} is indicated.
It uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
The function uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
}
\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.
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.plot.tree(feature_names = agaricus.train$data@Dimnames[[2]], model = bst)
}