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new documentation

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new import
This commit is contained in:
El Potaeto 2015-01-05 19:26:09 +01:00
parent b9799c6ac4
commit 3d068b4e1a
2 changed files with 44 additions and 0 deletions
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4
R-package/NAMESPACE
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@ -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)

40
R-package/man/xgb.plot.tree.Rd Normal file
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@ -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')
}