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Update lib version dependencies (for DiagrammeR mainly)

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Fix @export tag in each R file (for Roxygen 5, otherwise it doesn't work anymore)
Regerate Roxygen doc
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unknown 2015-11-07 21:01:28 +01:00
parent 635645c650
commit 0052b193cf
36 changed files with 123 additions and 126 deletions
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23
R-package/DESCRIPTION
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@ -3,16 +3,16 @@ Type: Package
Title: Extreme Gradient Boosting
Version: 0.4-2
Date: 2015-08-01
Author: Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>, Michael Benesty <michael@benesty.fr>
Author: Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>,
Michael Benesty <michael@benesty.fr>
Maintainer: Tong He <hetong007@gmail.com>
Description: Extreme Gradient Boosting, which is an
efficient implementation of gradient boosting framework.
This package is its R interface. The package includes efficient
linear model solver and tree learning algorithms. The package can automatically
do parallel computation on a single machine which could be more than 10 times faster
than existing gradient boosting packages. It supports various
objective functions, including regression, classification and ranking. The
package is made to be extensible, so that users are also allowed to define
Description: Extreme Gradient Boosting, which is an efficient implementation
of gradient boosting framework. This package is its R interface. The package
includes efficient linear model solver and tree learning algorithms. The package
can automatically do parallel computation on a single machine which could be
more than 10 times faster than existing gradient boosting packages. It supports
various objective functions, including regression, classification and ranking.
The package is made to be extensible, so that users are also allowed to define
their own objectives easily.
License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/dmlc/xgboost
@ -21,7 +21,7 @@ VignetteBuilder: knitr
Suggests:
knitr,
ggplot2 (>= 1.0.0),
DiagrammeR (>= 0.6),
DiagrammeR (>= 0.8.1),
Ckmeans.1d.dp (>= 3.3.1),
vcd (>= 1.3),
testthat
@ -30,6 +30,7 @@ Depends:
Imports:
Matrix (>= 1.1-0),
methods,
data.table (>= 1.9.4),
data.table (>= 1.9.6),
magrittr (>= 1.5),
stringr (>= 0.6.2)
RoxygenNote: 5.0.0

6
R-package/NAMESPACE
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@ -1,4 +1,4 @@
# Generated by roxygen2 (4.1.1): do not edit by hand
# Generated by roxygen2: do not edit by hand
export(getinfo)
export(setinfo)
@ -21,6 +21,10 @@ exportMethods(predict)
import(methods)
importClassesFrom(Matrix,dgCMatrix)
importClassesFrom(Matrix,dgeMatrix)
importFrom(DiagrammeR,create_edges)
importFrom(DiagrammeR,create_graph)
importFrom(DiagrammeR,create_nodes)
importFrom(DiagrammeR,render_graph)
importFrom(Matrix,cBind)
importFrom(Matrix,colSums)
importFrom(Matrix,sparseVector)

1
R-package/R/getinfo.xgb.DMatrix.R
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@ -23,7 +23,6 @@ setClass('xgb.DMatrix')
#' stopifnot(all(labels2 == 1-labels))
#' @rdname getinfo
#' @export
#'
getinfo <- function(object, ...){
UseMethod("getinfo")
}

1
R-package/R/predict.xgb.Booster.R
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@ -29,7 +29,6 @@ setClass("xgb.Booster",
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#' pred <- predict(bst, test$data)
#' @export
#'
setMethod("predict", signature = "xgb.Booster",
definition = function(object, newdata, missing = NA,
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {

1
R-package/R/setinfo.xgb.DMatrix.R
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@ -21,7 +21,6 @@
#' stopifnot(all(labels2 == 1-labels))
#' @rdname setinfo
#' @export
#'
setinfo <- function(object, ...){
UseMethod("setinfo")
}

1
R-package/R/slice.xgb.DMatrix.R
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@ -13,7 +13,6 @@ setClass('xgb.DMatrix')
#' dsub <- slice(dtrain, 1:3)
#' @rdname slice
#' @export
#'
slice <- function(object, ...){
UseMethod("slice")
}

1
R-package/R/xgb.DMatrix.R
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@ -17,7 +17,6 @@
#' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
#' @export
#'
xgb.DMatrix <- function(data, info = list(), missing = NA, ...) {
if (typeof(data) == "character") {
handle <- .Call("XGDMatrixCreateFromFile_R", data, as.integer(FALSE),

1
R-package/R/xgb.DMatrix.save.R
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@ -12,7 +12,6 @@
#' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
#' @export
#'
xgb.DMatrix.save <- function(DMatrix, fname) {
if (typeof(fname) != "character") {
stop("xgb.save: fname must be character")

1
R-package/R/xgb.cv.R
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@ -90,7 +90,6 @@
#' max.depth =3, eta = 1, objective = "binary:logistic")
#' print(history)
#' @export
#'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
prediction = FALSE, showsd = TRUE, metrics=list(),
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, print.every.n=1L,

1
R-package/R/xgb.dump.R
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@ -36,7 +36,6 @@
#' # print the model without saving it to a file
#' print(xgb.dump(bst))
#' @export
#'
xgb.dump <- function(model = NULL, fname = NULL, fmap = "", with.stats=FALSE) {
if (class(model) != "xgb.Booster") {
stop("model: argument must be type xgb.Booster")

1
R-package/R/xgb.load.R
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@ -15,7 +15,6 @@
#' bst <- xgb.load('xgb.model')
#' pred <- predict(bst, test$data)
#' @export
#'
xgb.load <- function(modelfile) {
if (is.null(modelfile))
stop("xgb.load: modelfile cannot be NULL")

1
R-package/R/xgb.save.R
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@ -16,7 +16,6 @@
#' bst <- xgb.load('xgb.model')
#' pred <- predict(bst, test$data)
#' @export
#'
xgb.save <- function(model, fname) {
if (typeof(fname) != "character") {
stop("xgb.save: fname must be character")

1
R-package/R/xgb.save.raw.R
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@ -16,7 +16,6 @@
#' bst <- xgb.load(raw)
#' pred <- predict(bst, test$data)
#' @export
#'
xgb.save.raw <- function(model) {
if (class(model) == "xgb.Booster"){
model <- model$handle

1
R-package/R/xgb.train.R
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@ -120,7 +120,6 @@
#' param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
#' @export
#'
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
obj = NULL, feval = NULL, verbose = 1, print.every.n=1L,
early.stop.round = NULL, maximize = NULL,

1
R-package/R/xgboost.R
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@ -58,7 +58,6 @@
#' pred <- predict(bst, test$data)
#'
#' @export
#'
xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
params = list(), nrounds,
verbose = 1, print.every.n = 1L, early.stop.round = NULL,

2
R-package/man/agaricus.test.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgboost.R
\docType{data}
\name{agaricus.test}

2
R-package/man/agaricus.train.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgboost.R
\docType{data}
\name{agaricus.train}

2
R-package/man/getinfo.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getinfo.xgb.DMatrix.R
\docType{methods}
\name{getinfo}

3
R-package/man/nrow-xgb.DMatrix-method.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nrow.xgb.DMatrix.R
\docType{methods}
\name{nrow,xgb.DMatrix-method}
@ -18,5 +18,6 @@ data(agaricus.train, package='xgboost')
train <- agaricus.train
dtrain <- xgb.DMatrix(train$data, label=train$label)
stopifnot(nrow(dtrain) == nrow(train$data))
}

2
R-package/man/predict-xgb.Booster-method.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.xgb.Booster.R
\docType{methods}
\name{predict,xgb.Booster-method}

2
R-package/man/predict-xgb.Booster.handle-method.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.xgb.Booster.handle.R
\docType{methods}
\name{predict,xgb.Booster.handle-method}

2
R-package/man/setinfo.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/setinfo.xgb.DMatrix.R
\docType{methods}
\name{setinfo}

2
R-package/man/slice.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/slice.xgb.DMatrix.R
\docType{methods}
\name{slice}

2
R-package/man/xgb.DMatrix.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.DMatrix.R
\name{xgb.DMatrix}
\alias{xgb.DMatrix}

2
R-package/man/xgb.DMatrix.save.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.DMatrix.save.R
\name{xgb.DMatrix.save}
\alias{xgb.DMatrix.save}

2
R-package/man/xgb.cv.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.cv.R
\name{xgb.cv}
\alias{xgb.cv}

2
R-package/man/xgb.dump.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.dump.R
\name{xgb.dump}
\alias{xgb.dump}

3
R-package/man/xgb.importance.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.importance.R
\name{xgb.importance}
\alias{xgb.importance}
@ -66,5 +66,6 @@ xgb.importance(train$data@Dimnames[[2]], model = bst)
# Same thing with co-occurence computation this time
xgb.importance(train$data@Dimnames[[2]], model = bst, data = train$data, label = train$label)
}

2
R-package/man/xgb.load.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.load.R
\name{xgb.load}
\alias{xgb.load}

3
R-package/man/xgb.model.dt.tree.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: 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}
@ -55,5 +55,6 @@ bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], model = bst)
}

3
R-package/man/xgb.plot.importance.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.plot.importance.R
\name{xgb.plot.importance}
\alias{xgb.plot.importance}
@ -36,5 +36,6 @@ bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#train$data@Dimnames[[2]] represents the column names of the sparse matrix.
importance_matrix <- xgb.importance(train$data@Dimnames[[2]], model = bst)
xgb.plot.importance(importance_matrix)
}

9
R-package/man/xgb.plot.tree.Rd
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@ -1,11 +1,11 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: 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, model = NULL,
n_first_tree = NULL, CSSstyle = NULL, width = NULL, height = NULL)
n_first_tree = NULL, width = NULL, 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}.}
@ -16,8 +16,6 @@ xgb.plot.tree(feature_names = NULL, filename_dump = NULL, model = NULL,
\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.}
\item{CSSstyle}{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.}
\item{width}{the width of the diagram in pixels.}
\item{height}{the height of the diagram in pixels.}
@ -39,7 +37,7 @@ 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 \href{https://github.com/knsv/mermaid/}{Mermaid} library for that purpose.
It uses \href{http://www.graphviz.org/}{GraphViz} library for that purpose.
}
\examples{
data(agaricus.train, package='xgboost')
@ -54,5 +52,6 @@ bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.plot.tree(agaricus.train$data@Dimnames[[2]], model = bst)
}

2
R-package/man/xgb.save.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.save.R
\name{xgb.save}
\alias{xgb.save}

2
R-package/man/xgb.save.raw.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.save.raw.R
\name{xgb.save.raw}
\alias{xgb.save.raw}

5
R-package/man/xgb.train.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.train.R
\name{xgb.train}
\alias{xgb.train}
@ -51,7 +51,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
\item \code{binary:logistic} logistic regression for binary classification. Output probability.
\item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
\item \code{num_class} set the number of classes. To use only with multiclass objectives.
\item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{tonum_class}.
\item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{num_class}.
\item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
\item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
}
@ -110,6 +110,7 @@ Number of threads can also be manually specified via \code{nthread} parameter.
\itemize{
\item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss}
\item \code{error} Binary classification error rate. It is calculated as \code{(wrong cases) / (all cases)}. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
\item \code{merror} Multiclass classification error rate. It is calculated as \code{(wrong cases) / (all cases)}.
\item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.

3
R-package/man/xgboost.Rd
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@ -1,4 +1,4 @@
% Generated by roxygen2 (4.1.1): do not edit by hand
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgboost.R
\name{xgboost}
\alias{xgboost}
@ -78,5 +78,6 @@ test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
pred <- predict(bst, test$data)
}