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replace iris in docs

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This commit is contained in:
hetong 2014-09-06 22:48:08 -07:00
parent ddf715953a
commit fbecd163c5
22 changed files with 117 additions and 76 deletions
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6
R-package/R/getinfo.xgb.DMatrix.R
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@ -5,9 +5,9 @@ setClass('xgb.DMatrix')
#' Get information of an xgb.DMatrix object #' Get information of an xgb.DMatrix object
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' train <- agaricus.train
#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dtrain <- xgb.DMatrix(train$data, label=train$label)
#' labels <- getinfo(dtrain, 'label') #' labels <- getinfo(dtrain, 'label')
#' setinfo(dtrain, 'label', 1-labels) #' setinfo(dtrain, 'label', 1-labels)
#' labels2 <- getinfo(dtrain, 'label') #' labels2 <- getinfo(dtrain, 'label')

10
R-package/R/predict.xgb.Booster.R
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@ -15,9 +15,13 @@ setClass("xgb.Booster")
#' only valid for gbtree, but not for gblinear. set it to be value bigger #' only valid for gbtree, but not for gblinear. set it to be value bigger
#' than 0. It will use all trees by default. #' than 0. It will use all trees by default.
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' data(agaricus.test, package='xgboost')
#' pred <- predict(bst, as.matrix(iris[,1:4])) #' train <- agaricus.train
#' test <- agaricus.test
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' eta = 1, nround = 2,objective = "binary:logistic")
#' pred <- predict(bst, test$data)
#' @export #' @export
#' #'
setMethod("predict", signature = "xgb.Booster", setMethod("predict", signature = "xgb.Booster",

6
R-package/R/setinfo.xgb.DMatrix.R
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@ -3,9 +3,9 @@
#' Set information of an xgb.DMatrix object #' Set information of an xgb.DMatrix object
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' train <- agaricus.train
#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dtrain <- xgb.DMatrix(train$data, label=train$label)
#' labels <- getinfo(dtrain, 'label') #' labels <- getinfo(dtrain, 'label')
#' setinfo(dtrain, 'label', 1-labels) #' setinfo(dtrain, 'label', 1-labels)
#' labels2 <- getinfo(dtrain, 'label') #' labels2 <- getinfo(dtrain, 'label')

6
R-package/R/slice.xgb.DMatrix.R
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@ -7,9 +7,9 @@ setClass('xgb.DMatrix')
#' orginal xgb.DMatrix object #' orginal xgb.DMatrix object
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' train <- agaricus.train
#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dtrain <- xgb.DMatrix(train$data, label=train$label)
#' dsub <- slice(dtrain, 1:3) #' dsub <- slice(dtrain, 1:3)
#' @rdname slice #' @rdname slice
#' @export #' @export

10
R-package/R/xgb.DMatrix.R
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@ -11,11 +11,11 @@
#' @param ... other information to pass to \code{info}. #' @param ... other information to pass to \code{info}.
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' train <- agaricus.train
#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dtrain <- xgb.DMatrix(train$data, label=train$label)
#' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
#' dtrain <- xgb.DMatrix('iris.xgb.DMatrix') #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
#' @export #' @export
#' #'
xgb.DMatrix <- function(data, info = list(), missing = 0, ...) { xgb.DMatrix <- function(data, info = list(), missing = 0, ...) {

10
R-package/R/xgb.DMatrix.save.R
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@ -6,11 +6,11 @@
#' @param fname the name of the binary file. #' @param fname the name of the binary file.
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' train <- agaricus.train
#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dtrain <- xgb.DMatrix(train$data, label=train$label)
#' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') #' xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
#' dtrain <- xgb.DMatrix('iris.xgb.DMatrix') #' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
#' @export #' @export
#' #'
xgb.DMatrix.save <- function(DMatrix, fname) { xgb.DMatrix.save <- function(DMatrix, fname) {

5
R-package/R/xgb.cv.R
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@ -46,6 +46,11 @@
#' #'
#' This function only accepts an \code{xgb.DMatrix} object as the input. #' This function only accepts an \code{xgb.DMatrix} object as the input.
#' #'
#' @examples
#' data(agaricus.train, package='xgboost')
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
#' "max_depth"=3, "eta"=1, "objective"="binary:logistic")
#' @export #' @export
#' #'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,

10
R-package/R/xgb.dump.R
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@ -12,9 +12,13 @@
#' #'
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' data(agaricus.test, package='xgboost')
#' xgb.dump(bst, 'iris.xgb.model.dump') #' train <- agaricus.train
#' test <- agaricus.test
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' eta = 1, nround = 2,objective = "binary:logistic")
#' xgb.dump(bst, 'xgb.model.dump')
#' @export #' @export
#' #'
xgb.dump <- function(model, fname, fmap = "") { xgb.dump <- function(model, fname, fmap = "") {

14
R-package/R/xgb.load.R
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@ -5,11 +5,15 @@
#' @param modelfile the name of the binary file. #' @param modelfile the name of the binary file.
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' data(agaricus.test, package='xgboost')
#' xgb.save(bst, 'iris.xgb.model') #' train <- agaricus.train
#' bst <- xgb.load('iris.xgb.model') #' test <- agaricus.test
#' pred <- predict(bst, as.matrix(iris[,1:4])) #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' eta = 1, nround = 2,objective = "binary:logistic")
#' xgb.save(bst, 'xgb.model')
#' bst <- xgb.load('xgb.model')
#' pred <- predict(bst, test$data)
#' @export #' @export
#' #'
xgb.load <- function(modelfile) { xgb.load <- function(modelfile) {

14
R-package/R/xgb.save.R
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@ -6,11 +6,15 @@
#' @param fname the name of the binary file. #' @param fname the name of the binary file.
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' data(agaricus.test, package='xgboost')
#' xgb.save(bst, 'iris.xgb.model') #' train <- agaricus.train
#' bst <- xgb.load('iris.xgb.model') #' test <- agaricus.test
#' pred <- predict(bst, as.matrix(iris[,1:4])) #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' eta = 1, nround = 2,objective = "binary:logistic")
#' xgb.save(bst, 'xgb.model')
#' bst <- xgb.load('xgb.model')
#' pred <- predict(bst, test$data)
#' @export #' @export
#' #'
xgb.save <- function(model, fname) { xgb.save <- function(model, fname) {

5
R-package/R/xgb.train.R
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@ -46,9 +46,8 @@
#' #'
#' #'
#' @examples #' @examples
#' data(iris) #' data(agaricus.train, package='xgboost')
#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
#' dtest <- dtrain #' dtest <- dtrain
#' watchlist <- list(eval = dtest, train = dtrain) #' watchlist <- list(eval = dtest, train = dtrain)
#' param <- list(max_depth = 2, eta = 1, silent = 1) #' param <- list(max_depth = 2, eta = 1, silent = 1)

6
R-package/man/getinfo.Rd
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@ -20,9 +20,9 @@ getinfo(object, ...)
Get information of an xgb.DMatrix object Get information of an xgb.DMatrix object
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
iris[,5] <- as.numeric(iris[,5]=='setosa') train <- agaricus.train
dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) dtrain <- xgb.DMatrix(train$data, label=train$label)
labels <- getinfo(dtrain, 'label') labels <- getinfo(dtrain, 'label')
setinfo(dtrain, 'label', 1-labels) setinfo(dtrain, 'label', 1-labels)
labels2 <- getinfo(dtrain, 'label') labels2 <- getinfo(dtrain, 'label')

10
R-package/man/predict-xgb.Booster-method.Rd
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@ -26,8 +26,12 @@ than 0. It will use all trees by default.}
Predicted values based on xgboost model object. Predicted values based on xgboost model object.
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) data(agaricus.test, package='xgboost')
pred <- predict(bst, as.matrix(iris[,1:4])) train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
pred <- predict(bst, test$data)
} }

6
R-package/man/setinfo.Rd
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@ -22,9 +22,9 @@ setinfo(object, ...)
Set information of an xgb.DMatrix object Set information of an xgb.DMatrix object
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
iris[,5] <- as.numeric(iris[,5]=='setosa') train <- agaricus.train
dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) dtrain <- xgb.DMatrix(train$data, label=train$label)
labels <- getinfo(dtrain, 'label') labels <- getinfo(dtrain, 'label')
setinfo(dtrain, 'label', 1-labels) setinfo(dtrain, 'label', 1-labels)
labels2 <- getinfo(dtrain, 'label') labels2 <- getinfo(dtrain, 'label')

6
R-package/man/slice.Rd
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@ -22,9 +22,9 @@ Get a new DMatrix containing the specified rows of
orginal xgb.DMatrix object orginal xgb.DMatrix object
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
iris[,5] <- as.numeric(iris[,5]=='setosa') train <- agaricus.train
dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) dtrain <- xgb.DMatrix(train$data, label=train$label)
dsub <- slice(dtrain, 1:3) dsub <- slice(dtrain, 1:3)
} }

10
R-package/man/xgb.DMatrix.Rd
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@ -19,10 +19,10 @@ indicating the data file.}
Contruct xgb.DMatrix object from dense matrix, sparse matrix or local file. Contruct xgb.DMatrix object from dense matrix, sparse matrix or local file.
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
iris[,5] <- as.numeric(iris[,5]=='setosa') train <- agaricus.train
dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) dtrain <- xgb.DMatrix(train$data, label=train$label)
xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
dtrain <- xgb.DMatrix('iris.xgb.DMatrix') dtrain <- xgb.DMatrix('xgb.DMatrix.data')
} }

10
R-package/man/xgb.DMatrix.save.Rd
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@ -14,10 +14,10 @@ xgb.DMatrix.save(DMatrix, fname)
Save xgb.DMatrix object to binary file Save xgb.DMatrix object to binary file
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
iris[,5] <- as.numeric(iris[,5]=='setosa') train <- agaricus.train
dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) dtrain <- xgb.DMatrix(train$data, label=train$label)
xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') xgb.DMatrix.save(dtrain, 'xgb.DMatrix.data')
dtrain <- xgb.DMatrix('iris.xgb.DMatrix') dtrain <- xgb.DMatrix('xgb.DMatrix.data')
} }

6
R-package/man/xgb.cv.Rd
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@ -63,4 +63,10 @@ Number of threads can also be manually specified via "nthread" parameter.
This function only accepts an \code{xgb.DMatrix} object as the input. This function only accepts an \code{xgb.DMatrix} object as the input.
} }
\examples{
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
"max_depth"=3, "eta"=1, "objective"="binary:logistic")
}

10
R-package/man/xgb.dump.Rd
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@ -20,8 +20,12 @@ xgb.dump(model, fname, fmap = "")
Save a xgboost model to text file. Could be parsed later. Save a xgboost model to text file. Could be parsed later.
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) data(agaricus.test, package='xgboost')
xgb.dump(bst, 'iris.xgb.model.dump') train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump')
} }

14
R-package/man/xgb.load.Rd
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@ -12,10 +12,14 @@ xgb.load(modelfile)
Load xgboost model from the binary model file Load xgboost model from the binary model file
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) data(agaricus.test, package='xgboost')
xgb.save(bst, 'iris.xgb.model') train <- agaricus.train
bst <- xgb.load('iris.xgb.model') test <- agaricus.test
pred <- predict(bst, as.matrix(iris[,1:4])) bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
xgb.save(bst, 'xgb.model')
bst <- xgb.load('xgb.model')
pred <- predict(bst, test$data)
} }

14
R-package/man/xgb.save.Rd
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@ -14,10 +14,14 @@ xgb.save(model, fname)
Save xgboost model from xgboost or xgb.train Save xgboost model from xgboost or xgb.train
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) data(agaricus.test, package='xgboost')
xgb.save(bst, 'iris.xgb.model') train <- agaricus.train
bst <- xgb.load('iris.xgb.model') test <- agaricus.test
pred <- predict(bst, as.matrix(iris[,1:4])) bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
xgb.save(bst, 'xgb.model')
bst <- xgb.load('xgb.model')
pred <- predict(bst, test$data)
} }

5
R-package/man/xgb.train.Rd
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@ -58,9 +58,8 @@ It supports advanced features such as watchlist, customized objective function,
therefore it is more flexible than \code{\link{xgboost}}. therefore it is more flexible than \code{\link{xgboost}}.
} }
\examples{ \examples{
data(iris) data(agaricus.train, package='xgboost')
iris[,5] <- as.numeric(iris[,5]=='setosa') dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
dtest <- dtrain dtest <- dtrain
watchlist <- list(eval = dtest, train = dtrain) watchlist <- list(eval = dtest, train = dtrain)
param <- list(max_depth = 2, eta = 1, silent = 1) param <- list(max_depth = 2, eta = 1, silent = 1)