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[R] Finalizes switch to markdown doc (#10733)

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Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Michael Mayer
2024-08-26 19:25:06 +02:00
committed by GitHub
parent 479ae8081b
commit 074cad2343
37 changed files with 1257 additions and 1200 deletions
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7
R-package/man/agaricus.test.Rd
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@@ -16,11 +16,10 @@ This data set is originally from the Mushroom data set,
UCI Machine Learning Repository.
}
\details{
This data set includes the following fields:
It includes the following fields:
\itemize{
\item \code{label} the label for each record
\item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
\item \code{label}: The label for each record.
\item \code{data}: A sparse Matrix of 'dgCMatrix' class with 126 columns.
}
}
\references{

7
R-package/man/agaricus.train.Rd
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@@ -16,11 +16,10 @@ This data set is originally from the Mushroom data set,
UCI Machine Learning Repository.
}
\details{
This data set includes the following fields:
It includes the following fields:
\itemize{
\item \code{label} the label for each record
\item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
\item \code{label}: The label for each record.
\item \code{data}: A sparse Matrix of 'dgCMatrix' class with 126 columns.
}
}
\references{

7
R-package/man/dim.xgb.DMatrix.Rd
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@@ -13,13 +13,14 @@
Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
}
\details{
Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
Note: since \code{\link[=nrow]{nrow()}} and \code{\link[=ncol]{ncol()}} internally use \code{\link[=dim]{dim()}}, they can also
be directly used with an \code{xgb.DMatrix} object.
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
train <- agaricus.train
dtrain <- xgb.DMatrix(train$data, label=train$label, nthread = 2)
dtrain <- xgb.DMatrix(train$data, label = train$label, nthread = 2)
stopifnot(nrow(dtrain) == nrow(train$data))
stopifnot(ncol(dtrain) == ncol(train$data))

17
R-package/man/dimnames.xgb.DMatrix.Rd
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@@ -10,26 +10,27 @@
\method{dimnames}{xgb.DMatrix}(x) <- value
}
\arguments{
\item{x}{object of class \code{xgb.DMatrix}}
\item{x}{Object of class \code{xgb.DMatrix}.}
\item{value}{a list of two elements: the first one is ignored
\item{value}{A list of two elements: the first one is ignored
and the second one is column names}
}
\description{
Only column names are supported for \code{xgb.DMatrix}, thus setting of
row names would have no effect and returned row names would be NULL.
row names would have no effect and returned row names would be \code{NULL}.
}
\details{
Generic \code{dimnames} methods are used by \code{colnames}.
Since row names are irrelevant, it is recommended to use \code{colnames} directly.
Generic \code{\link[=dimnames]{dimnames()}} methods are used by \code{\link[=colnames]{colnames()}}.
Since row names are irrelevant, it is recommended to use \code{\link[=colnames]{colnames()}} directly.
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
train <- agaricus.train
dtrain <- xgb.DMatrix(train$data, label=train$label, nthread = 2)
dtrain <- xgb.DMatrix(train$data, label = train$label, nthread = 2)
dimnames(dtrain)
colnames(dtrain)
colnames(dtrain) <- make.names(1:ncol(train$data))
print(dtrain, verbose=TRUE)
print(dtrain, verbose = TRUE)
}

66
R-package/man/getinfo.Rd
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@@ -22,34 +22,34 @@ setinfo(object, name, info)
\method{setinfo}{xgb.DMatrix}(object, name, info)
}
\arguments{
\item{object}{Object of class \code{xgb.DMatrix} of \code{xgb.Booster}.}
\item{object}{Object of class \code{xgb.DMatrix} or \code{xgb.Booster}.}
\item{name}{the name of the information field to get (see details)}
\item{name}{The name of the information field to get (see details).}
\item{info}{the specific field of information to set}
\item{info}{The specific field of information to set.}
}
\value{
For \code{getinfo}, will return the requested field. For \code{setinfo}, will always return value \code{TRUE}
if it succeeds.
For \code{getinfo()}, will return the requested field. For \code{setinfo()},
will always return value \code{TRUE} if it succeeds.
}
\description{
Get or set information of xgb.DMatrix and xgb.Booster objects
}
\details{
The \code{name} field can be one of the following for \code{xgb.DMatrix}:
\itemize{
\item \code{label}
\item \code{weight}
\item \code{base_margin}
\item \code{label_lower_bound}
\item \code{label_upper_bound}
\item \code{group}
\item \code{feature_type}
\item \code{feature_name}
\item \code{nrow}
\item label
\item weight
\item base_margin
\item label_lower_bound
\item label_upper_bound
\item group
\item feature_type
\item feature_name
\item nrow
}
See the documentation for \link{xgb.DMatrix} for more information about these fields.
See the documentation for \code{\link[=xgb.DMatrix]{xgb.DMatrix()}} for more information about these fields.
For \code{xgb.Booster}, can be one of the following:
\itemize{
@@ -57,17 +57,18 @@ For \code{xgb.Booster}, can be one of the following:
\item \code{feature_name}
}
Note that, while 'qid' cannot be retrieved, it's possible to get the equivalent 'group'
Note that, while 'qid' cannot be retrieved, it is possible to get the equivalent 'group'
for a DMatrix that had 'qid' assigned.
\bold{Important}: when calling \code{setinfo}, the objects are modified in-place. See
\link{xgb.copy.Booster} for an idea of this in-place assignment works.
\strong{Important}: when calling \code{\link[=setinfo]{setinfo()}}, the objects are modified in-place. See
\code{\link[=xgb.copy.Booster]{xgb.copy.Booster()}} for an idea of this in-place assignment works.
See the documentation for \link{xgb.DMatrix} for possible fields that can be set
See the documentation for \code{\link[=xgb.DMatrix]{xgb.DMatrix()}} for possible fields that can be set
(which correspond to arguments in that function).
Note that the following fields are allowed in the construction of an \code{xgb.DMatrix}
but \bold{aren't} allowed here:\itemize{
but \strong{are not} allowed here:
\itemize{
\item data
\item missing
\item silent
@@ -75,19 +76,22 @@ but \bold{aren't} allowed here:\itemize{
}
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
labels <- getinfo(dtrain, 'label')
setinfo(dtrain, 'label', 1-labels)
labels <- getinfo(dtrain, "label")
setinfo(dtrain, "label", 1 - labels)
labels2 <- getinfo(dtrain, "label")
stopifnot(all(labels2 == 1 - labels))
data(agaricus.train, package = "xgboost")
labels2 <- getinfo(dtrain, 'label')
stopifnot(all(labels2 == 1-labels))
data(agaricus.train, package='xgboost')
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
labels <- getinfo(dtrain, 'label')
setinfo(dtrain, 'label', 1-labels)
labels2 <- getinfo(dtrain, 'label')
stopifnot(all.equal(labels2, 1-labels))
labels <- getinfo(dtrain, "label")
setinfo(dtrain, "label", 1 - labels)
labels2 <- getinfo(dtrain, "label")
stopifnot(all.equal(labels2, 1 - labels))
}

3
R-package/man/predict.xgb.Booster.Rd
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@@ -157,7 +157,8 @@ dimension should produce practically the same result as \code{predcontrib = TRUE
For multi-class and multi-target, will be a 4D array with dimensions \verb{[nrows, ngroups, nfeats+1, nfeats+1]}
}
If passing \code{strict_shape=FALSE}, the result is always an array:\itemize{
If passing \code{strict_shape=FALSE}, the result is always an array:
\itemize{
\item For normal predictions, the dimension is \verb{[nrows, ngroups]}.
\item For \code{predcontrib=TRUE}, the dimension is \verb{[nrows, ngroups, nfeats+1]}.
\item For \code{predinteraction=TRUE}, the dimension is \verb{[nrows, ngroups, nfeats+1, nfeats+1]}.

13
R-package/man/print.xgb.DMatrix.Rd
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@@ -7,21 +7,22 @@
\method{print}{xgb.DMatrix}(x, verbose = FALSE, ...)
}
\arguments{
\item{x}{an xgb.DMatrix object}
\item{x}{An xgb.DMatrix object.}
\item{verbose}{whether to print colnames (when present)}
\item{verbose}{Whether to print colnames (when present).}
\item{...}{not currently used}
\item{...}{Not currently used.}
}
\description{
Print information about xgb.DMatrix.
Currently it displays dimensions and presence of info-fields and colnames.
}
\examples{
data(agaricus.train, package='xgboost')
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
data(agaricus.train, package = "xgboost")
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
dtrain
print(dtrain, verbose=TRUE)
print(dtrain, verbose = TRUE)
}

24
R-package/man/print.xgb.cv.Rd
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@@ -7,25 +7,33 @@
\method{print}{xgb.cv.synchronous}(x, verbose = FALSE, ...)
}
\arguments{
\item{x}{an \code{xgb.cv.synchronous} object}
\item{x}{An \code{xgb.cv.synchronous} object.}
\item{verbose}{whether to print detailed data}
\item{verbose}{Whether to print detailed data.}
\item{...}{passed to \code{data.table.print}}
\item{...}{Passed to \code{data.table.print()}.}
}
\description{
Prints formatted results of \code{xgb.cv}.
Prints formatted results of \code{\link[=xgb.cv]{xgb.cv()}}.
}
\details{
When not verbose, it would only print the evaluation results,
including the best iteration (when available).
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
train <- agaricus.train
cv <- xgb.cv(data = xgb.DMatrix(train$data, label = train$label), nfold = 5, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
cv <- xgb.cv(
data = xgb.DMatrix(train$data, label = train$label),
nfold = 5,
max_depth = 2,
eta = 1,
nthread = 2,
nrounds = 2,
objective = "binary:logistic"
)
print(cv)
print(cv, verbose=TRUE)
print(cv, verbose = TRUE)
}

2
R-package/man/xgb.Callback.Rd
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@@ -116,7 +116,7 @@ model ended, in which case this will be larger than 1.
It should match with argument \code{nrounds} passed to \code{\link[=xgb.train]{xgb.train()}} or \code{\link[=xgb.cv]{xgb.cv()}}.
Note that boosting might be interrupted before reaching this last iteration, for
example by using the early stopping callback \link{xgb.cb.early.stop}.
example by using the early stopping callback \code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}}.
\item iteration Index of the iteration number that is being executed (first iteration
will be the same as parameter \code{begin_iteration}, then next one will add +1, and so on).
\item iter_feval Evaluation metrics for \code{evals} that were supplied, either

38
R-package/man/xgb.DMatrix.Rd
View File

@@ -57,20 +57,20 @@ was constructed.
Other column types are not supported.
\item CSR matrices, as class \code{dgRMatrix} from package \code{Matrix}.
\item CSC matrices, as class \code{dgCMatrix} from package \code{Matrix}. These are \bold{not} supported for
\item CSC matrices, as class \code{dgCMatrix} from package \code{Matrix}. These are \strong{not} supported for
'xgb.QuantileDMatrix'.
\item Single-row CSR matrices, as class \code{dsparseVector} from package \code{Matrix}, which is interpreted
as a single row (only when making predictions from a fitted model).
\item Text files in a supported format, passed as a \code{character} variable containing the URI path to
the file, with an optional format specifier.
These are \bold{not} supported for \code{xgb.QuantileDMatrix}. Supported formats are:\itemize{
\item XGBoost's own binary format for DMatrices, as produced by \link{xgb.DMatrix.save}.
These are \strong{not} supported for \code{xgb.QuantileDMatrix}. Supported formats are:\itemize{
\item XGBoost's own binary format for DMatrices, as produced by \code{\link[=xgb.DMatrix.save]{xgb.DMatrix.save()}}.
\item SVMLight (a.k.a. LibSVM) format for CSR matrices. This format can be signaled by suffix
\code{?format=libsvm} at the end of the file path. It will be the default format if not
otherwise specified.
\item CSV files (comma-separated values). This format can be specified by adding suffix
\code{?format=csv} at the end ofthe file path. It will \bold{not} be auto-deduced from file extensions.
\code{?format=csv} at the end ofthe file path. It will \strong{not} be auto-deduced from file extensions.
}
Be aware that the format of the file will not be auto-deduced - for example, if a file is named 'file.csv',
@@ -99,25 +99,24 @@ so it doesn't make sense to assign weights to individual data points.}
In the case of multi-output models, one can also pass multi-dimensional base_margin.}
\item{missing}{A float value to represents missing values in data (not used when creating DMatrix
from text files).
It is useful to change when a zero, infinite, or some other extreme value represents missing
values in data.}
from text files). It is useful to change when a zero, infinite, or some other
extreme value represents missing values in data.}
\item{silent}{whether to suppress printing an informational message after loading from a file.}
\item{feature_names}{Set names for features. Overrides column names in data
frame and matrix.
\item{feature_names}{Set names for features. Overrides column names in data frame and matrix.
Note: columns are not referenced by name when calling \code{predict}, so the column order there
must be the same as in the DMatrix construction, regardless of the column names.}
\item{feature_types}{Set types for features.
If \code{data} is a \code{data.frame} and passing \code{feature_types} is not supplied, feature types will be deduced
automatically from the column types.
If \code{data} is a \code{data.frame} and passing \code{feature_types} is not supplied,
feature types will be deduced automatically from the column types.
Otherwise, one can pass a character vector with the same length as number of columns in \code{data},
with the following possible values:\itemize{
with the following possible values:
\itemize{
\item "c", which represents categorical columns.
\item "q", which represents numeric columns.
\item "int", which represents integer columns.
@@ -128,9 +127,9 @@ Note that, while categorical types are treated differently from the rest for mod
purposes, the other types do not influence the generated model, but have effects in other
functionalities such as feature importances.
\bold{Important}: categorical features, if specified manually through \code{feature_types}, must
\strong{Important}: Categorical features, if specified manually through \code{feature_types}, must
be encoded as integers with numeration starting at zero, and the same encoding needs to be
applied when passing data to \code{predict}. Even if passing \code{factor} types, the encoding will
applied when passing data to \code{\link[=predict]{predict()}}. Even if passing \code{factor} types, the encoding will
not be saved, so make sure that \code{factor} columns passed to \code{predict} have the same \code{levels}.}
\item{nthread}{Number of threads used for creating DMatrix.}
@@ -154,7 +153,7 @@ how the file was split beforehand. Default to row.
This is not used when \code{data} is not a URI.}
\item{ref}{The training dataset that provides quantile information, needed when creating
validation/test dataset with \code{xgb.QuantileDMatrix}. Supplying the training DMatrix
validation/test dataset with \code{\link[=xgb.QuantileDMatrix]{xgb.QuantileDMatrix()}}. Supplying the training DMatrix
as a reference means that the same quantisation applied to the training data is
applied to the validation/test data}
@@ -169,23 +168,24 @@ subclass 'xgb.QuantileDMatrix'.
}
\description{
Construct an 'xgb.DMatrix' object from a given data source, which can then be passed to functions
such as \link{xgb.train} or \link{predict.xgb.Booster}.
such as \code{\link[=xgb.train]{xgb.train()}} or \code{\link[=predict]{predict()}}.
}
\details{
Function 'xgb.QuantileDMatrix' will construct a DMatrix with quantization for the histogram
Function \code{xgb.QuantileDMatrix()} will construct a DMatrix with quantization for the histogram
method already applied to it, which can be used to reduce memory usage (compared to using a
a regular DMatrix first and then creating a quantization out of it) when using the histogram
method (\code{tree_method = "hist"}, which is the default algorithm), but is not usable for the
sorted-indices method (\code{tree_method = "exact"}), nor for the approximate method
(\code{tree_method = "approx"}).
Note that DMatrix objects are not serializable through R functions such as \code{saveRDS} or \code{save}.
Note that DMatrix objects are not serializable through R functions such as \code{\link[=saveRDS]{saveRDS()}} or \code{\link[=save]{save()}}.
If a DMatrix gets serialized and then de-serialized (for example, when saving data in an R session or caching
chunks in an Rmd file), the resulting object will not be usable anymore and will need to be reconstructed
from the original source of data.
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
## Keep the number of threads to 1 for examples
nthread <- 1
data.table::setDTthreads(nthread)

5
R-package/man/xgb.DMatrix.hasinfo.Rd
View File

@@ -16,11 +16,10 @@ Checks whether an xgb.DMatrix object has a given field assigned to
it, such as weights, labels, etc.
}
\examples{
library(xgboost)
x <- matrix(1:10, nrow = 5)
dm <- xgb.DMatrix(x, nthread = 1)
# 'dm' so far doesn't have any fields set
# 'dm' so far does not have any fields set
xgb.DMatrix.hasinfo(dm, "label")
# Fields can be added after construction
@@ -28,5 +27,5 @@ setinfo(dm, "label", 1:5)
xgb.DMatrix.hasinfo(dm, "label")
}
\seealso{
\link{xgb.DMatrix}, \link{getinfo.xgb.DMatrix}, \link{setinfo.xgb.DMatrix}
\code{\link[=xgb.DMatrix]{xgb.DMatrix()}}, \code{\link[=getinfo.xgb.DMatrix]{getinfo.xgb.DMatrix()}}, \code{\link[=setinfo.xgb.DMatrix]{setinfo.xgb.DMatrix()}}
}

3
R-package/man/xgb.DMatrix.save.Rd
View File

@@ -16,7 +16,8 @@ Save xgb.DMatrix object to binary file
}
\examples{
\dontshow{RhpcBLASctl::omp_set_num_threads(1)}
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
fname <- file.path(tempdir(), "xgb.DMatrix.data")
xgb.DMatrix.save(dtrain, fname)

43
R-package/man/xgb.DataBatch.Rd
View File

@@ -21,16 +21,17 @@ xgb.DataBatch(
\arguments{
\item{data}{Batch of data belonging to this batch.
Note that not all of the input types supported by \link{xgb.DMatrix} are possible
to pass here. Supported types are:\itemize{
Note that not all of the input types supported by \code{\link[=xgb.DMatrix]{xgb.DMatrix()}} are possible
to pass here. Supported types are:
\itemize{
\item \code{matrix}, with types \code{numeric}, \code{integer}, and \code{logical}. Note that for types
\code{integer} and \code{logical}, missing values might not be automatically recognized as
as such - see the documentation for parameter \code{missing} in \link{xgb.ExternalDMatrix}
as such - see the documentation for parameter \code{missing} in \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}
for details on this.
\item \code{data.frame}, with the same types as supported by 'xgb.DMatrix' and same
conversions applied to it. See the documentation for parameter \code{data} in
\link{xgb.DMatrix} for details on it.
\item CSR matrices, as class \code{dgRMatrix} from package \code{Matrix}.
\code{\link[=xgb.DMatrix]{xgb.DMatrix()}} for details on it.
\item CSR matrices, as class \code{dgRMatrix} from package "Matrix".
}}
\item{label}{Label of the training data. For classification problems, should be passed encoded as
@@ -47,19 +48,19 @@ so it doesn't make sense to assign weights to individual data points.}
In the case of multi-output models, one can also pass multi-dimensional base_margin.}
\item{feature_names}{Set names for features. Overrides column names in data
frame and matrix.
\item{feature_names}{Set names for features. Overrides column names in data frame and matrix.
Note: columns are not referenced by name when calling \code{predict}, so the column order there
must be the same as in the DMatrix construction, regardless of the column names.}
\item{feature_types}{Set types for features.
If \code{data} is a \code{data.frame} and passing \code{feature_types} is not supplied, feature types will be deduced
automatically from the column types.
If \code{data} is a \code{data.frame} and passing \code{feature_types} is not supplied,
feature types will be deduced automatically from the column types.
Otherwise, one can pass a character vector with the same length as number of columns in \code{data},
with the following possible values:\itemize{
with the following possible values:
\itemize{
\item "c", which represents categorical columns.
\item "q", which represents numeric columns.
\item "int", which represents integer columns.
@@ -70,9 +71,9 @@ Note that, while categorical types are treated differently from the rest for mod
purposes, the other types do not influence the generated model, but have effects in other
functionalities such as feature importances.
\bold{Important}: categorical features, if specified manually through \code{feature_types}, must
\strong{Important}: Categorical features, if specified manually through \code{feature_types}, must
be encoded as integers with numeration starting at zero, and the same encoding needs to be
applied when passing data to \code{predict}. Even if passing \code{factor} types, the encoding will
applied when passing data to \code{\link[=predict]{predict()}}. Even if passing \code{factor} types, the encoding will
not be saved, so make sure that \code{factor} columns passed to \code{predict} have the same \code{levels}.}
\item{group}{Group size for all ranking group.}
@@ -87,24 +88,24 @@ not be saved, so make sure that \code{factor} columns passed to \code{predict} h
}
\value{
An object of class \code{xgb.DataBatch}, which is just a list containing the
data and parameters passed here. It does \bold{not} inherit from \code{xgb.DMatrix}.
data and parameters passed here. It does \strong{not} inherit from \code{xgb.DMatrix}.
}
\description{
Helper function to supply data in batches of a data iterator when
constructing a DMatrix from external memory through \link{xgb.ExternalDMatrix}
or through \link{xgb.QuantileDMatrix.from_iterator}.
constructing a DMatrix from external memory through \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}
or through \code{\link[=xgb.QuantileDMatrix.from_iterator]{xgb.QuantileDMatrix.from_iterator()}}.
This function is \bold{only} meant to be called inside of a callback function (which
is passed as argument to function \link{xgb.DataIter} to construct a data iterator)
This function is \strong{only} meant to be called inside of a callback function (which
is passed as argument to function \code{\link[=xgb.DataIter]{xgb.DataIter()}} to construct a data iterator)
when constructing a DMatrix through external memory - otherwise, one should call
\link{xgb.DMatrix} or \link{xgb.QuantileDMatrix}.
\code{\link[=xgb.DMatrix]{xgb.DMatrix()}} or \code{\link[=xgb.QuantileDMatrix]{xgb.QuantileDMatrix()}}.
The object that results from calling this function directly is \bold{not} like
The object that results from calling this function directly is \strong{not} like
an \code{xgb.DMatrix} - i.e. cannot be used to train a model, nor to get predictions - only
possible usage is to supply data to an iterator, from which a DMatrix is then constructed.
For more information and for example usage, see the documentation for \link{xgb.ExternalDMatrix}.
For more information and for example usage, see the documentation for \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}.
}
\seealso{
\link{xgb.DataIter}, \link{xgb.ExternalDMatrix}.
\code{\link[=xgb.DataIter]{xgb.DataIter()}}, \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}.
}

15
R-package/man/xgb.DataIter.Rd
View File

@@ -13,14 +13,15 @@ used to keep track of variables to determine how to handle the batches.
For example, one might want to keep track of an iteration number in this environment in order
to know which part of the data to pass next.}
\item{f_next}{\verb{function(env)} which is responsible for:\itemize{
\item{f_next}{\verb{function(env)} which is responsible for:
\itemize{
\item Accessing or retrieving the next batch of data in the iterator.
\item Supplying this data by calling function \link{xgb.DataBatch} on it and returning the result.
\item Supplying this data by calling function \code{\link[=xgb.DataBatch]{xgb.DataBatch()}} on it and returning the result.
\item Keeping track of where in the iterator batch it is or will go next, which can for example
be done by modifiying variables in the \code{env} variable that is passed here.
\item Signaling whether there are more batches to be consumed or not, by returning \code{NULL}
when the stream of data ends (all batches in the iterator have been consumed), or the result from
calling \link{xgb.DataBatch} when there are more batches in the line to be consumed.
calling \code{\link[=xgb.DataBatch]{xgb.DataBatch()}} when there are more batches in the line to be consumed.
}}
\item{f_reset}{\verb{function(env)} which is responsible for reseting the data iterator
@@ -32,7 +33,7 @@ Note that, after resetting the iterator, the batches will be accessed again, so
}
\value{
An \code{xgb.DataIter} object, containing the same inputs supplied here, which can then
be passed to \link{xgb.ExternalDMatrix}.
be passed to \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}.
}
\description{
Interface to create a custom data iterator in order to construct a DMatrix
@@ -41,11 +42,11 @@ from external memory.
This function is responsible for generating an R object structure containing callback
functions and an environment shared with them.
The output structure from this function is then meant to be passed to \link{xgb.ExternalDMatrix},
The output structure from this function is then meant to be passed to \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}},
which will consume the data and create a DMatrix from it by executing the callback functions.
For more information, and for a usage example, see the documentation for \link{xgb.ExternalDMatrix}.
For more information, and for a usage example, see the documentation for \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}.
}
\seealso{
\link{xgb.ExternalDMatrix}, \link{xgb.DataBatch}.
\code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}, \code{\link[=xgb.DataBatch]{xgb.DataBatch()}}.
}

21
R-package/man/xgb.ExternalDMatrix.Rd
View File

@@ -12,7 +12,7 @@ xgb.ExternalDMatrix(
)
}
\arguments{
\item{data_iterator}{A data iterator structure as returned by \link{xgb.DataIter},
\item{data_iterator}{A data iterator structure as returned by \code{\link[=xgb.DataIter]{xgb.DataIter()}},
which includes an environment shared between function calls, and functions to access
the data in batches on-demand.}
@@ -20,14 +20,14 @@ the data in batches on-demand.}
\item{missing}{A float value to represents missing values in data.
Note that, while functions like \link{xgb.DMatrix} can take a generic \code{NA} and interpret it
Note that, while functions like \code{\link[=xgb.DMatrix]{xgb.DMatrix()}} can take a generic \code{NA} and interpret it
correctly for different types like \code{numeric} and \code{integer}, if an \code{NA} value is passed here,
it will not be adapted for different input types.
For example, in R \code{integer} types, missing values are represented by integer number \code{-2147483648}
(since machine 'integer' types do not have an inherent 'NA' value) - hence, if one passes \code{NA},
which is interpreted as a floating-point NaN by 'xgb.ExternalDMatrix' and by
'xgb.QuantileDMatrix.from_iterator', these integer missing values will not be treated as missing.
which is interpreted as a floating-point NaN by \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}} and by
\code{\link[=xgb.QuantileDMatrix.from_iterator]{xgb.QuantileDMatrix.from_iterator()}}, these integer missing values will not be treated as missing.
This should not pose any problem for \code{numeric} types, since they do have an inheret NaN value.}
\item{nthread}{Number of threads used for creating DMatrix.}
@@ -37,23 +37,22 @@ An 'xgb.DMatrix' object, with subclass 'xgb.ExternalDMatrix', in which the data
held internally but accessed through the iterator when needed.
}
\description{
Create a special type of xgboost 'DMatrix' object from external data
supplied by an \link{xgb.DataIter} object, potentially passed in batches from a
Create a special type of XGBoost 'DMatrix' object from external data
supplied by an \code{\link[=xgb.DataIter]{xgb.DataIter()}} object, potentially passed in batches from a
bigger set that might not fit entirely in memory.
The data supplied by the iterator is accessed on-demand as needed, multiple times,
without being concatenated, but note that fields like 'label' \bold{will} be
without being concatenated, but note that fields like 'label' \strong{will} be
concatenated from multiple calls to the data iterator.
For more information, see the guide 'Using XGBoost External Memory Version':
\url{https://xgboost.readthedocs.io/en/stable/tutorials/external_memory.html}
}
\examples{
library(xgboost)
data(mtcars)
# this custom environment will be passed to the iterator
# functions at each call. It's up to the user to keep
# This custom environment will be passed to the iterator
# functions at each call. It is up to the user to keep
# track of the iteration number in this environment.
iterator_env <- as.environment(
list(
@@ -118,5 +117,5 @@ pred_dm <- predict(model, dm)
pred_mat <- predict(model, as.matrix(mtcars[, -1]))
}
\seealso{
\link{xgb.DataIter}, \link{xgb.DataBatch}, \link{xgb.QuantileDMatrix.from_iterator}
\code{\link[=xgb.DataIter]{xgb.DataIter()}}, \code{\link[=xgb.DataBatch]{xgb.DataBatch()}}, \code{\link[=xgb.QuantileDMatrix.from_iterator]{xgb.QuantileDMatrix.from_iterator()}}
}

22
R-package/man/xgb.QuantileDMatrix.from_iterator.Rd
View File

@@ -13,26 +13,26 @@ xgb.QuantileDMatrix.from_iterator(
)
}
\arguments{
\item{data_iterator}{A data iterator structure as returned by \link{xgb.DataIter},
\item{data_iterator}{A data iterator structure as returned by \code{\link[=xgb.DataIter]{xgb.DataIter()}},
which includes an environment shared between function calls, and functions to access
the data in batches on-demand.}
\item{missing}{A float value to represents missing values in data.
Note that, while functions like \link{xgb.DMatrix} can take a generic \code{NA} and interpret it
Note that, while functions like \code{\link[=xgb.DMatrix]{xgb.DMatrix()}} can take a generic \code{NA} and interpret it
correctly for different types like \code{numeric} and \code{integer}, if an \code{NA} value is passed here,
it will not be adapted for different input types.
For example, in R \code{integer} types, missing values are represented by integer number \code{-2147483648}
(since machine 'integer' types do not have an inherent 'NA' value) - hence, if one passes \code{NA},
which is interpreted as a floating-point NaN by 'xgb.ExternalDMatrix' and by
'xgb.QuantileDMatrix.from_iterator', these integer missing values will not be treated as missing.
which is interpreted as a floating-point NaN by \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}} and by
\code{\link[=xgb.QuantileDMatrix.from_iterator]{xgb.QuantileDMatrix.from_iterator()}}, these integer missing values will not be treated as missing.
This should not pose any problem for \code{numeric} types, since they do have an inheret NaN value.}
\item{nthread}{Number of threads used for creating DMatrix.}
\item{ref}{The training dataset that provides quantile information, needed when creating
validation/test dataset with \code{xgb.QuantileDMatrix}. Supplying the training DMatrix
validation/test dataset with \code{\link[=xgb.QuantileDMatrix]{xgb.QuantileDMatrix()}}. Supplying the training DMatrix
as a reference means that the same quantisation applied to the training data is
applied to the validation/test data}
@@ -46,20 +46,20 @@ An 'xgb.DMatrix' object, with subclass 'xgb.QuantileDMatrix'.
}
\description{
Create an \code{xgb.QuantileDMatrix} object (exact same class as would be returned by
calling function \link{xgb.QuantileDMatrix}, with the same advantages and limitations) from
external data supplied by an \link{xgb.DataIter} object, potentially passed in batches from
a bigger set that might not fit entirely in memory, same way as \link{xgb.ExternalDMatrix}.
calling function \code{\link[=xgb.QuantileDMatrix]{xgb.QuantileDMatrix()}}, with the same advantages and limitations) from
external data supplied by \code{\link[=xgb.DataIter]{xgb.DataIter()}}, potentially passed in batches from
a bigger set that might not fit entirely in memory, same way as \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}}.
Note that, while external data will only be loaded through the iterator (thus the full data
might not be held entirely in-memory), the quantized representation of the data will get
created in-memory, being concatenated from multiple calls to the data iterator. The quantized
version is typically lighter than the original data, so there might be cases in which this
representation could potentially fit in memory even if the full data doesn't.
representation could potentially fit in memory even if the full data does not.
For more information, see the guide 'Using XGBoost External Memory Version':
\url{https://xgboost.readthedocs.io/en/stable/tutorials/external_memory.html}
}
\seealso{
\link{xgb.DataIter}, \link{xgb.DataBatch}, \link{xgb.ExternalDMatrix},
\link{xgb.QuantileDMatrix}
\code{\link[=xgb.DataIter]{xgb.DataIter()}}, \code{\link[=xgb.DataBatch]{xgb.DataBatch()}}, \code{\link[=xgb.ExternalDMatrix]{xgb.ExternalDMatrix()}},
\code{\link[=xgb.QuantileDMatrix]{xgb.QuantileDMatrix()}}
}

2
R-package/man/xgb.attr.Rd
View File

@@ -51,7 +51,7 @@ Also, setting an attribute that has the same name as one of XGBoost's parameters
change the value of that parameter for a model.
Use \code{\link[=xgb.parameters<-]{xgb.parameters<-()}} to set or change model parameters.
The \code{\link[=xgb.attributes<-]{xgb.attributes<-()}} setter either updates the existing or adds one or several attributes,
The \verb{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
but it doesn't delete the other existing attributes.
Important: since this modifies the booster's C object, semantics for assignment here

158
R-package/man/xgb.cv.Rd
View File

@@ -26,141 +26,136 @@ xgb.cv(
)
}
\arguments{
\item{params}{the list of parameters. The complete list of parameters is
available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
is a shorter summary:
\item{params}{The list of parameters. The complete list of parameters is available in the
\href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}.
Below is a shorter summary:
\itemize{
\item \code{objective} objective function, common ones are
\item \code{objective}: Objective function, common ones are
\itemize{
\item \code{reg:squarederror} Regression with squared loss.
\item \code{binary:logistic} logistic regression for classification.
\item See \code{\link[=xgb.train]{xgb.train}()} for complete list of objectives.
}
\item \code{eta} step size of each boosting step
\item \code{max_depth} maximum depth of the tree
\item \code{nthread} number of thread used in training, if not set, all threads are used
\item \code{reg:squarederror}: Regression with squared loss.
\item \code{binary:logistic}: Logistic regression for classification.
}
See \code{\link{xgb.train}} for further details.
See also demo/ for walkthrough example in R.
See \code{\link[=xgb.train]{xgb.train()}} for complete list of objectives.
\item \code{eta}: Step size of each boosting step
\item \code{max_depth}: Maximum depth of the tree
\item \code{nthread}: Number of threads used in training. If not set, all threads are used
}
See \code{\link[=xgb.train]{xgb.train()}} for further details.
See also demo for walkthrough example in R.
Note that, while \code{params} accepts a \code{seed} entry and will use such parameter for model training if
supplied, this seed is not used for creation of train-test splits, which instead rely on R's own RNG
system - thus, for reproducible results, one needs to call the \code{set.seed} function beforehand.}
system - thus, for reproducible results, one needs to call the \code{\link[=set.seed]{set.seed()}} function beforehand.}
\item{data}{An \code{xgb.DMatrix} object, with corresponding fields like \code{label} or bounds as required
for model training by the objective.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ Note that only the basic `xgb.DMatrix` class is supported - variants such as `xgb.QuantileDMatrix`
or `xgb.ExternalDMatrix` are not supported here.
}\if{html}{\out{</div>}}}
Note that only the basic \code{xgb.DMatrix} class is supported - variants such as \code{xgb.QuantileDMatrix}
or \code{xgb.ExternalDMatrix} are not supported here.}
\item{nrounds}{the max number of iterations}
\item{nrounds}{The max number of iterations.}
\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}
\item{nfold}{The original dataset is randomly partitioned into \code{nfold} equal size subsamples.}
\item{prediction}{A logical value indicating whether to return the test fold predictions
from each CV model. This parameter engages the \code{\link{xgb.cb.cv.predict}} callback.}
from each CV model. This parameter engages the \code{\link[=xgb.cb.cv.predict]{xgb.cb.cv.predict()}} callback.}
\item{showsd}{\code{boolean}, whether to show standard deviation of cross validation}
\item{showsd}{Logical value whether to show standard deviation of cross validation.}
\item{metrics, }{list of evaluation metrics to be used in cross validation,
\item{metrics}{List of evaluation metrics to be used in cross validation,
when it is not specified, the evaluation metric is chosen according to objective function.
Possible options are:
\itemize{
\item \code{error} binary classification error rate
\item \code{rmse} Rooted mean square error
\item \code{logloss} negative log-likelihood function
\item \code{mae} Mean absolute error
\item \code{mape} Mean absolute percentage error
\item \code{auc} Area under curve
\item \code{aucpr} Area under PR curve
\item \code{merror} Exact matching error, used to evaluate multi-class classification
\item \code{error}: Binary classification error rate
\item \code{rmse}: Root mean square error
\item \code{logloss}: Negative log-likelihood function
\item \code{mae}: Mean absolute error
\item \code{mape}: Mean absolute percentage error
\item \code{auc}: Area under curve
\item \code{aucpr}: Area under PR curve
\item \code{merror}: Exact matching error used to evaluate multi-class classification
}}
\item{obj}{customized objective function. Returns gradient and second order
\item{obj}{Customized objective function. Returns gradient and second order
gradient with given prediction and dtrain.}
\item{feval}{customized evaluation function. Returns
\code{list(metric='metric-name', value='metric-value')} with given
prediction and dtrain.}
\item{feval}{Customized evaluation function. Returns
\code{list(metric='metric-name', value='metric-value')} with given prediction and dtrain.}
\item{stratified}{A \code{boolean} indicating whether sampling of folds should be stratified
\item{stratified}{Logical flag indicating whether sampling of folds should be stratified
by the values of outcome labels. For real-valued labels in regression objectives,
stratification will be done by discretizing the labels into up to 5 buckets beforehand.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ If passing "auto", will be set to `TRUE` if the objective in `params` is a classification
objective (from XGBoost's built-in objectives, doesn't apply to custom ones), and to
`FALSE` otherwise.
If passing "auto", will be set to \code{TRUE} if the objective in \code{params} is a classification
objective (from XGBoost's built-in objectives, doesn't apply to custom ones), and to
\code{FALSE} otherwise.
This parameter is ignored when `data` has a `group` field - in such case, the splitting
will be based on whole groups (note that this might make the folds have different sizes).
This parameter is ignored when \code{data} has a \code{group} field - in such case, the splitting
will be based on whole groups (note that this might make the folds have different sizes).
Value `TRUE` here is \\bold\{not\} supported for custom objectives.
}\if{html}{\out{</div>}}}
Value \code{TRUE} here is \strong{not} supported for custom objectives.}
\item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
(each element must be a vector of test fold's indices). When folds are supplied,
the \code{nfold} and \code{stratified} parameters are ignored.
\item{folds}{List with pre-defined CV folds (each element must be a vector of test fold's indices).
When folds are supplied, the \code{nfold} and \code{stratified} parameters are ignored.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ If `data` has a `group` field and the objective requires this field, each fold (list element)
must additionally have two attributes (retrievable through \link{attributes}) named `group_test`
and `group_train`, which should hold the `group` to assign through \link{setinfo.xgb.DMatrix} to
the resulting DMatrices.
}\if{html}{\out{</div>}}}
If \code{data} has a \code{group} field and the objective requires this field, each fold (list element)
must additionally have two attributes (retrievable through \code{attributes}) named \code{group_test}
and \code{group_train}, which should hold the \code{group} to assign through \code{\link[=setinfo.xgb.DMatrix]{setinfo.xgb.DMatrix()}} to
the resulting DMatrices.}
\item{train_folds}{\code{list} list specifying which indicies to use for training. If \code{NULL}
\item{train_folds}{List specifying which indices to use for training. If \code{NULL}
(the default) all indices not specified in \code{folds} will be used for training.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ This is not supported when `data` has `group` field.
}\if{html}{\out{</div>}}}
This is not supported when \code{data} has \code{group} field.}
\item{verbose}{\code{boolean}, print the statistics during the process}
\item{verbose}{Logical flag. Should statistics be printed during the process?}
\item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
\item{print_every_n}{Print each nth iteration evaluation messages when \code{verbose > 0}.
Default is 1 which means all messages are printed. This parameter is passed to the
\code{\link{xgb.cb.print.evaluation}} callback.}
\code{\link[=xgb.cb.print.evaluation]{xgb.cb.print.evaluation()}} callback.}
\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
If set to an integer \code{k}, training with a validation set will stop if the performance
doesn't improve for \code{k} rounds.
Setting this parameter engages the \code{\link{xgb.cb.early.stop}} callback.}
Setting this parameter engages the \code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}} callback.}
\item{maximize}{If \code{feval} and \code{early_stopping_rounds} are set,
then this parameter must be set as well.
When it is \code{TRUE}, it means the larger the evaluation score the better.
This parameter is passed to the \code{\link{xgb.cb.early.stop}} callback.}
This parameter is passed to the \code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}} callback.}
\item{callbacks}{a list of callback functions to perform various task during boosting.
See \code{\link{xgb.Callback}}. Some of the callbacks are automatically created depending on the
\item{callbacks}{A list of callback functions to perform various task during boosting.
See \code{\link[=xgb.Callback]{xgb.Callback()}}. Some of the callbacks are automatically created depending on the
parameters' values. User can provide either existing or their own callback methods in order
to customize the training process.}
\item{...}{other parameters to pass to \code{params}.}
\item{...}{Other parameters to pass to \code{params}.}
}
\value{
An object of class \code{xgb.cv.synchronous} with the following elements:
An object of class 'xgb.cv.synchronous' with the following elements:
\itemize{
\item \code{call} a function call.
\item \code{params} parameters that were passed to the xgboost library. Note that it does not
capture parameters changed by the \code{\link{xgb.cb.reset.parameters}} callback.
\item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
\item \code{call}: Function call.
\item \code{params}: Parameters that were passed to the xgboost library. Note that it does not
capture parameters changed by the \code{\link[=xgb.cb.reset.parameters]{xgb.cb.reset.parameters()}} callback.
\item \code{evaluation_log}: Evaluation history stored as a \code{data.table} with the
first column corresponding to iteration number and the rest corresponding to the
CV-based evaluation means and standard deviations for the training and test CV-sets.
It is created by the \code{\link{xgb.cb.evaluation.log}} callback.
\item \code{niter} number of boosting iterations.
\item \code{nfeatures} number of features in training data.
\item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
It is created by the \code{\link[=xgb.cb.evaluation.log]{xgb.cb.evaluation.log()}} callback.
\item \code{niter}: Number of boosting iterations.
\item \code{nfeatures}: Number of features in training data.
\item \code{folds}: The list of CV folds' indices - either those passed through the \code{folds}
parameter or randomly generated.
\item \code{best_iteration} iteration number with the best evaluation metric value
\item \code{best_iteration}: Iteration number with the best evaluation metric value
(only available with early stopping).
}
Plus other potential elements that are the result of callbacks, such as a list \code{cv_predict} with
a sub-element \code{pred} when passing \code{prediction = TRUE}, which is added by the \link{xgb.cb.cv.predict}
a sub-element \code{pred} when passing \code{prediction = TRUE}, which is added by the \code{\link[=xgb.cb.cv.predict]{xgb.cb.cv.predict()}}
callback (note that one can also pass it manually under \code{callbacks} with different settings,
such as saving also the models created during cross validation); or a list \code{early_stop} which
will contain elements such as \code{best_iteration} when using the early stopping callback (\link{xgb.cb.early.stop}).
will contain elements such as \code{best_iteration} when using the early stopping callback (\code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}}).
}
\description{
The cross validation function of xgboost.
@@ -179,11 +174,20 @@ All observations are used for both training and validation.
Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
cv <- xgb.cv(data = dtrain, nrounds = 3, nthread = 2, nfold = 5, metrics = list("rmse","auc"),
max_depth = 3, eta = 1, objective = "binary:logistic")
cv <- xgb.cv(
data = dtrain,
nrounds = 3,
nthread = 2,
nfold = 5,
metrics = list("rmse","auc"),
max_depth = 3,
eta = 1,objective = "binary:logistic"
)
print(cv)
print(cv, verbose=TRUE)
print(cv, verbose = TRUE)
}

2
R-package/man/xgb.get.DMatrix.data.Rd
View File

@@ -7,7 +7,7 @@
xgb.get.DMatrix.data(dmat)
}
\arguments{
\item{dmat}{An \code{xgb.DMatrix} object, as returned by \link{xgb.DMatrix}.}
\item{dmat}{An \code{xgb.DMatrix} object, as returned by \code{\link[=xgb.DMatrix]{xgb.DMatrix()}}.}
}
\value{
The data held in the DMatrix, as a sparse CSR matrix (class \code{dgRMatrix}

4
R-package/man/xgb.get.DMatrix.num.non.missing.Rd
View File

@@ -7,10 +7,10 @@
xgb.get.DMatrix.num.non.missing(dmat)
}
\arguments{
\item{dmat}{An \code{xgb.DMatrix} object, as returned by \link{xgb.DMatrix}.}
\item{dmat}{An \code{xgb.DMatrix} object, as returned by \code{\link[=xgb.DMatrix]{xgb.DMatrix()}}.}
}
\value{
The number of non-missing entries in the DMatrix
The number of non-missing entries in the DMatrix.
}
\description{
Get Number of Non-Missing Entries in DMatrix

21
R-package/man/xgb.get.DMatrix.qcut.Rd
View File

@@ -7,15 +7,14 @@
xgb.get.DMatrix.qcut(dmat, output = c("list", "arrays"))
}
\arguments{
\item{dmat}{An \code{xgb.DMatrix} object, as returned by \link{xgb.DMatrix}.}
\item{dmat}{An \code{xgb.DMatrix} object, as returned by \code{\link[=xgb.DMatrix]{xgb.DMatrix()}}.}
\item{output}{Output format for the quantile cuts. Possible options are:\itemize{
\item \code{"list"} will return the output as a list with one entry per column, where
each column will have a numeric vector with the cuts. The list will be named if
\code{dmat} has column names assigned to it.
\item{output}{Output format for the quantile cuts. Possible options are:
\itemize{
\item "list"\verb{will return the output as a list with one entry per column, where each column will have a numeric vector with the cuts. The list will be named if}dmat` has column names assigned to it.
\item \code{"arrays"} will return a list with entries \code{indptr} (base-0 indexing) and
\code{data}. Here, the cuts for column 'i' are obtained by slicing 'data' from entries
\code{indptr[i]+1} to \code{indptr[i+1]}.
\code{ indptr[i]+1} to \code{indptr[i+1]}.
}}
}
\value{
@@ -23,7 +22,7 @@ The quantile cuts, in the format specified by parameter \code{output}.
}
\description{
Get the quantile cuts (a.k.a. borders) from an \code{xgb.DMatrix}
that has been quantized for the histogram method (\code{tree_method="hist"}).
that has been quantized for the histogram method (\code{tree_method = "hist"}).
These cuts are used in order to assign observations to bins - i.e. these are ordered
boundaries which are used to determine assignment condition \verb{border_low < x < border_high}.
@@ -36,8 +35,8 @@ which will be output in sorted order from lowest to highest.
Different columns can have different numbers of bins according to their range.
}
\examples{
library(xgboost)
data(mtcars)
y <- mtcars$mpg
x <- as.matrix(mtcars[, -1])
dm <- xgb.DMatrix(x, label = y, nthread = 1)
@@ -45,11 +44,7 @@ dm <- xgb.DMatrix(x, label = y, nthread = 1)
# DMatrix is not quantized right away, but will be once a hist model is generated
model <- xgb.train(
data = dm,
params = list(
tree_method = "hist",
max_bin = 8,
nthread = 1
),
params = list(tree_method = "hist", max_bin = 8, nthread = 1),
nrounds = 3
)

2
R-package/man/xgb.plot.multi.trees.Rd
View File

@@ -15,7 +15,7 @@ xgb.plot.multi.trees(
}
\arguments{
\item{model}{Object of class \code{xgb.Booster}. If it contains feature names (they can be set through
\link{setinfo}), they will be used in the output from this function.}
\code{\link[=setinfo]{setinfo()}}, they will be used in the output from this function.}
\item{features_keep}{Number of features to keep in each position of the multi trees,
by default 5.}

2
R-package/man/xgb.plot.tree.Rd
View File

@@ -17,7 +17,7 @@ xgb.plot.tree(
}
\arguments{
\item{model}{Object of class \code{xgb.Booster}. If it contains feature names (they can be set through
\link{setinfo}), they will be used in the output from this function.}
\code{\link[=setinfo]{setinfo()}}, they will be used in the output from this function.}
\item{trees}{An integer vector of tree indices that should be used.
The default (\code{NULL}) uses all trees.

20
R-package/man/xgb.slice.DMatrix.Rd
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@@ -3,36 +3,36 @@
\name{xgb.slice.DMatrix}
\alias{xgb.slice.DMatrix}
\alias{[.xgb.DMatrix}
\title{Get a new DMatrix containing the specified rows of
original xgb.DMatrix object}
\title{Slice DMatrix}
\usage{
xgb.slice.DMatrix(object, idxset, allow_groups = FALSE)
\method{[}{xgb.DMatrix}(object, idxset, colset = NULL)
}
\arguments{
\item{object}{Object of class "xgb.DMatrix".}
\item{object}{Object of class \code{xgb.DMatrix}.}
\item{idxset}{An integer vector of indices of rows needed (base-1 indexing).}
\item{allow_groups}{Whether to allow slicing an \code{xgb.DMatrix} with \code{group} (or
equivalently \code{qid}) field. Note that in such case, the result will not have
the groups anymore - they need to be set manually through \code{setinfo}.}
the groups anymore - they need to be set manually through \code{\link[=setinfo]{setinfo()}}.}
\item{colset}{currently not used (columns subsetting is not available)}
\item{colset}{Currently not used (columns subsetting is not available).}
}
\description{
Get a new DMatrix containing the specified rows of
original xgb.DMatrix object
Get a new DMatrix containing the specified rows of original xgb.DMatrix object.
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.train, package = "xgboost")
dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
dsub <- xgb.slice.DMatrix(dtrain, 1:42)
labels1 <- getinfo(dsub, 'label')
labels1 <- getinfo(dsub, "label")
dsub <- dtrain[1:42, ]
labels2 <- getinfo(dsub, 'label')
labels2 <- getinfo(dsub, "label")
all.equal(labels1, labels2)
}

347
R-package/man/xgb.train.Rd
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@@ -24,106 +24,100 @@ xgb.train(
}
\arguments{
\item{params}{the list of parameters. The complete list of parameters is
available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
is a shorter summary:
\enumerate{
\item General Parameters
}
available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}.
Below is a shorter summary:
\strong{1. General Parameters}
\itemize{
\item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
}
\enumerate{
\item Booster Parameters
\item \code{booster}: Which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
}
2.1. Parameters for Tree Booster
\strong{2. Booster Parameters}
\strong{2.1. Parameters for Tree Booster}
\itemize{
\item{ \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1}
\item \code{eta}: The learning rate: scale the contribution of each tree by a factor of \verb{0 < eta < 1}
when it is added to the current approximation.
Used to prevent overfitting by making the boosting process more conservative.
Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model
more robust to overfitting but slower to compute. Default: 0.3}
\item{ \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree.
the larger, the more conservative the algorithm will be.}
\item \code{max_depth} maximum depth of a tree. Default: 6
\item{\code{min_child_weight} minimum sum of instance weight (hessian) needed in a child.
more robust to overfitting but slower to compute. Default: 0.3.
\item \code{gamma}: Minimum loss reduction required to make a further partition on a leaf node of the tree.
the larger, the more conservative the algorithm will be.
\item \code{max_depth}: Maximum depth of a tree. Default: 6.
\item \code{min_child_weight}: Minimum sum of instance weight (hessian) needed in a child.
If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight,
then the building process will give up further partitioning.
In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node.
The larger, the more conservative the algorithm will be. Default: 1}
\item{ \code{subsample} subsample ratio of the training instance.
The larger, the more conservative the algorithm will be. Default: 1.
\item \code{subsample}: Subsample ratio of the training instance.
Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees
and this will prevent overfitting. It makes computation shorter (because less data to analyse).
It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1}
\item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
\item \code{lambda} L2 regularization term on weights. Default: 1
\item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
\item{ \code{num_parallel_tree} Experimental parameter. number of trees to grow per round.
Useful to test Random Forest through XGBoost
(set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly.
Default: 1}
\item{ \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length
It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1.
\item \code{colsample_bytree}: Subsample ratio of columns when constructing each tree. Default: 1.
\item \code{lambda}: L2 regularization term on weights. Default: 1.
\item \code{alpha}: L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0.
\item \code{num_parallel_tree}: Experimental parameter. number of trees to grow per round.
Useful to test Random Forest through XGBoost.
(set \code{colsample_bytree < 1}, \code{subsample < 1} and \code{round = 1}) accordingly.
Default: 1.
\item \code{monotone_constraints}: A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length
equals to the number of features in the training data.
\code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.}
\item{ \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions.
\code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.
\item \code{interaction_constraints}: A list of vectors specifying feature indices of permitted interactions.
Each item of the list represents one permitted interaction where specified features are allowed to interact with each other.
Feature index values should start from \code{0} (\code{0} references the first column).
Leave argument unspecified for no interaction constraints.}
Leave argument unspecified for no interaction constraints.
}
2.2. Parameters for Linear Booster
\strong{2.2. Parameters for Linear Booster}
\itemize{
\item \code{lambda} L2 regularization term on weights. Default: 0
\item \code{lambda_bias} L2 regularization term on bias. Default: 0
\item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
}
\enumerate{
\item Task Parameters
\item \code{lambda}: L2 regularization term on weights. Default: 0.
\item \code{lambda_bias}: L2 regularization term on bias. Default: 0.
\item \code{alpha}: L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0.
}
\strong{3. Task Parameters}
\itemize{
\item{ \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it.
The default objective options are below:
\item \code{objective}: Specifies the learning task and the corresponding learning objective.
users can pass a self-defined function to it. The default objective options are below:
\itemize{
\item \code{reg:squarederror} Regression with squared loss (Default).
\item{ \code{reg:squaredlogerror}: regression with squared log loss \eqn{1/2 * (log(pred + 1) - log(label + 1))^2}.
\item \code{reg:squarederror}: Regression with squared loss (default).
\item \code{reg:squaredlogerror}: Regression with squared log loss \eqn{1/2 \cdot (\log(pred + 1) - \log(label + 1))^2}.
All inputs are required to be greater than -1.
Also, see metric rmsle for possible issue with this objective.}
\item \code{reg:logistic} logistic regression.
\item \code{reg:pseudohubererror}: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
\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{binary:hinge}: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
\item{ \code{count:poisson}: Poisson regression for count data, output mean of Poisson distribution.
\code{max_delta_step} is set to 0.7 by default in poisson regression (used to safeguard optimization).}
\item{ \code{survival:cox}: Cox regression for right censored survival time data (negative values are considered right censored).
Also, see metric rmsle for possible issue with this objective.
\item \code{reg:logistic}: Logistic regression.
\item \code{reg:pseudohubererror}: Regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
\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{binary:hinge}: Hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
\item \code{count:poisson}: Poisson regression for count data, output mean of Poisson distribution.
The parameter \code{max_delta_step} is set to 0.7 by default in poisson regression
(used to safeguard optimization).
\item \code{survival:cox}: Cox regression for right censored survival time data (negative values are considered right censored).
Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional
hazard function \code{h(t) = h0(t) * HR)}.}
\item{ \code{survival:aft}: Accelerated failure time model for censored survival time data. See
hazard function \eqn{h(t) = h_0(t) \cdot HR}.
\item \code{survival:aft}: Accelerated failure time model for censored survival time data. See
\href{https://xgboost.readthedocs.io/en/latest/tutorials/aft_survival_analysis.html}{Survival Analysis with Accelerated Failure Time}
for details.}
\item \code{aft_loss_distribution}: Probability Density Function used by \code{survival:aft} and \code{aft-nloglik} metric.
\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 - 1}.}
\item{ \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be
for details.
The parameter \code{aft_loss_distribution} specifies the Probability Density Function
used by \code{survival:aft} and the \code{aft-nloglik} metric.
\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 - 1}.
\item \code{multi:softprob}: Same as softmax, but prediction outputs a vector of ndata * nclass elements, 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.
\item{ \code{rank:ndcg}: Use LambdaMART to perform list-wise ranking where
\href{https://en.wikipedia.org/wiki/Discounted_cumulative_gain}{Normalized Discounted Cumulative Gain (NDCG)} is maximized.}
\item{ \code{rank:map}: Use LambdaMART to perform list-wise ranking where
to each class.
\item \code{rank:pairwise}: Set XGBoost to do ranking task by minimizing the pairwise loss.
\item \code{rank:ndcg}: Use LambdaMART to perform list-wise ranking where
\href{https://en.wikipedia.org/wiki/Discounted_cumulative_gain}{Normalized Discounted Cumulative Gain (NDCG)} is maximized.
\item \code{rank:map}: Use LambdaMART to perform list-wise ranking where
\href{https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision}{Mean Average Precision (MAP)}
is maximized.}
\item{ \code{reg:gamma}: gamma regression with log-link.
Output is a mean of gamma distribution.
is maximized.
\item \code{reg:gamma}: Gamma regression with log-link. Output is a mean of gamma distribution.
It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be
\href{https://en.wikipedia.org/wiki/Gamma_distribution#Applications}{gamma-distributed}.}
\item{ \code{reg:tweedie}: Tweedie regression with log-link.
\href{https://en.wikipedia.org/wiki/Gamma_distribution#Applications}{gamma-distributed}.
\item \code{reg:tweedie}: Tweedie regression with log-link.
It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be
\href{https://en.wikipedia.org/wiki/Tweedie_distribution#Applications}{Tweedie-distributed}.}
\href{https://en.wikipedia.org/wiki/Tweedie_distribution#Applications}{Tweedie-distributed}.
}
For custom objectives, one should pass a function taking as input the current predictions (as a numeric
@@ -134,91 +128,85 @@ For multi-valued custom objectives, should have shape \verb{[nrows, ntargets]}.
the Hessian will be clipped, so one might consider using the expected Hessian (Fisher information) if the
objective is non-convex.
See the tutorials \href{https://xgboost.readthedocs.io/en/stable/tutorials/custom_metric_obj.html}{
Custom Objective and Evaluation Metric} and \href{https://xgboost.readthedocs.io/en/stable/tutorials/advanced_custom_obj}{
Advanced Usage of Custom Objectives} for more information about custom objectives.
}
\item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
\item{ \code{eval_metric} evaluation metrics for validation data.
See the tutorials \href{https://xgboost.readthedocs.io/en/stable/tutorials/custom_metric_obj.html}{Custom Objective and Evaluation Metric}
and \href{https://xgboost.readthedocs.io/en/stable/tutorials/advanced_custom_obj}{Advanced Usage of Custom Objectives}
for more information about custom objectives.
\item \code{base_score}: The initial prediction score of all instances, global bias. Default: 0.5.
\item \code{eval_metric}: Evaluation metrics for validation data.
Users can pass a self-defined function to it.
Default: metric will be assigned according to objective
(rmse for regression, and error for classification, mean average precision for ranking).
List is provided in detail section.}
List is provided in detail section.
}}
\item{data}{training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
\code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.}
\item{data}{Training dataset. \code{xgb.train()} accepts only an \code{xgb.DMatrix} as the input.
\code{\link[=xgboost]{xgboost()}}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.}
\item{nrounds}{max number of boosting iterations.}
\item{nrounds}{Max number of boosting iterations.}
\item{evals}{Named list of \code{xgb.DMatrix} datasets to use for evaluating model performance.
Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
of these datasets during each boosting iteration, and stored in the end as a field named
\code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
\code{\link{xgb.cb.print.evaluation}} callback is engaged, the performance results are continuously
\code{\link[=xgb.cb.print.evaluation]{xgb.cb.print.evaluation()}} callback is engaged, the performance results are continuously
printed out during the training.
E.g., specifying \code{evals=list(validation1=mat1, validation2=mat2)} allows to track
the performance of each round's model on mat1 and mat2.}
\item{obj}{customized objective function. Should take two arguments: the first one will be the
\item{obj}{Customized objective function. Should take two arguments: the first one will be the
current predictions (either a numeric vector or matrix depending on the number of targets / classes),
and the second one will be the \code{data} DMatrix object that is used for training.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ It should return a list with two elements `grad` and `hess` (in that order), as either
numeric vectors or numeric matrices depending on the number of targets / classes (same
dimension as the predictions that are passed as first argument).
}\if{html}{\out{</div>}}}
It should return a list with two elements \code{grad} and \code{hess} (in that order), as either
numeric vectors or numeric matrices depending on the number of targets / classes (same
dimension as the predictions that are passed as first argument).}
\item{feval}{customized evaluation function. Just like \code{obj}, should take two arguments, with
\item{feval}{Customized evaluation function. Just like \code{obj}, should take two arguments, with
the first one being the predictions and the second one the \code{data} DMatrix.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ Should return a list with two elements `metric` (name that will be displayed for this metric,
should be a string / character), and `value` (the number that the function calculates, should
be a numeric scalar).
Should return a list with two elements \code{metric} (name that will be displayed for this metric,
should be a string / character), and \code{value} (the number that the function calculates, should
be a numeric scalar).
Note that even if passing `feval`, objectives also have an associated default metric that
will be evaluated in addition to it. In order to disable the built-in metric, one can pass
parameter `disable_default_eval_metric = TRUE`.
}\if{html}{\out{</div>}}}
Note that even if passing \code{feval}, objectives also have an associated default metric that
will be evaluated in addition to it. In order to disable the built-in metric, one can pass
parameter \code{disable_default_eval_metric = TRUE}.}
\item{verbose}{If 0, xgboost will stay silent. If 1, it will print information about performance.
If 2, some additional information will be printed out.
Note that setting \code{verbose > 0} automatically engages the
\code{xgb.cb.print.evaluation(period=1)} callback function.}
\item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
\item{print_every_n}{Print each nth iteration evaluation messages when \code{verbose>0}.
Default is 1 which means all messages are printed. This parameter is passed to the
\code{\link{xgb.cb.print.evaluation}} callback.}
\code{\link[=xgb.cb.print.evaluation]{xgb.cb.print.evaluation()}} callback.}
\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
If set to an integer \code{k}, training with a validation set will stop if the performance
doesn't improve for \code{k} rounds.
Setting this parameter engages the \code{\link{xgb.cb.early.stop}} callback.}
doesn't improve for \code{k} rounds. Setting this parameter engages the \code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}} callback.}
\item{maximize}{If \code{feval} and \code{early_stopping_rounds} are set,
then this parameter must be set as well.
\item{maximize}{If \code{feval} and \code{early_stopping_rounds} are set, then this parameter must be set as well.
When it is \code{TRUE}, it means the larger the evaluation score the better.
This parameter is passed to the \code{\link{xgb.cb.early.stop}} callback.}
This parameter is passed to the \code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}} callback.}
\item{save_period}{when it is non-NULL, model is saved to disk after every \code{save_period} rounds,
0 means save at the end. The saving is handled by the \code{\link{xgb.cb.save.model}} callback.}
\item{save_period}{When not \code{NULL}, model is saved to disk after every \code{save_period} rounds.
0 means save at the end. The saving is handled by the \code{\link[=xgb.cb.save.model]{xgb.cb.save.model()}} callback.}
\item{save_name}{the name or path for periodically saved model file.}
\item{xgb_model}{a previously built model to continue the training from.
\item{xgb_model}{A previously built model to continue the training from.
Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
file with a previously saved model.}
\item{callbacks}{a list of callback functions to perform various task during boosting.
See \code{\link{xgb.Callback}}. Some of the callbacks are automatically created depending on the
\item{callbacks}{A list of callback functions to perform various task during boosting.
See \code{\link[=xgb.Callback]{xgb.Callback()}}. Some of the callbacks are automatically created depending on the
parameters' values. User can provide either existing or their own callback methods in order
to customize the training process.
\if{html}{\out{<div class="sourceCode">}}\preformatted{ Note that some callbacks might try to leave attributes in the resulting model object,
such as an evaluation log (a `data.table` object) - be aware that these objects are kept
as R attributes, and thus do not get saved when using XGBoost's own serializaters like
\link{xgb.save} (but are kept when using R serializers like \link{saveRDS}).
}\if{html}{\out{</div>}}}
Note that some callbacks might try to leave attributes in the resulting model object,
such as an evaluation log (a \code{data.table} object) - be aware that these objects are kept
as R attributes, and thus do not get saved when using XGBoost's own serializaters like
\code{\link[=xgb.save]{xgb.save()}} (but are kept when using R serializers like \code{\link[=saveRDS]{saveRDS()}}).}
\item{...}{other parameters to pass to \code{params}.}
}
@@ -226,19 +214,18 @@ to customize the training process.
An object of class \code{xgb.Booster}.
}
\description{
\code{xgb.train} is an advanced interface for training an xgboost model.
The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
\code{xgb.train()} is an advanced interface for training an xgboost model.
The \code{\link[=xgboost]{xgboost()}} function is a simpler wrapper for \code{xgb.train()}.
}
\details{
These are the training functions for \code{xgboost}.
These are the training functions for \code{\link[=xgboost]{xgboost()}}.
The \code{xgb.train} interface supports advanced features such as \code{evals},
The \code{xgb.train()} interface supports advanced features such as \code{evals},
customized objective and evaluation metric functions, therefore it is more flexible
than the \code{xgboost} interface.
than the \code{\link[=xgboost]{xgboost()}} interface.
Parallelization is automatically enabled if \code{OpenMP} is present.
Number of threads can also be manually specified via the \code{nthread}
parameter.
Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via the \code{nthread} parameter.
While in other interfaces, the default random seed defaults to zero, in R, if a parameter \code{seed}
is not manually supplied, it will generate a random seed through R's own random number generator,
@@ -251,49 +238,49 @@ User may set one or several \code{eval_metric} parameters.
Note that when using a customized metric, only this single metric can be used.
The following is the list of built-in metrics for which XGBoost provides optimized implementation:
\itemize{
\item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
\item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
\item \code{rmse}: Root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss}: Negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss}: Multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
\item \code{error}: Binary classification error rate. It is calculated as \verb{(# wrong cases) / (# all cases)}.
By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
Different threshold (e.g., 0.) could be specified as "error@0."
\item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
\item \code{mae} Mean absolute error
\item \code{mape} Mean absolute percentage error
\item{ \code{auc} Area under the curve.
\url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.}
\item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
\item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
Different threshold (e.g., 0.) could be specified as \verb{error@0}.
\item \code{merror}: Multiclass classification error rate. It is calculated as \verb{(# wrong cases) / (# all cases)}.
\item \code{mae}: Mean absolute error.
\item \code{mape}: Mean absolute percentage error.
\item \code{auc}: Area under the curve.
\url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
\item \code{aucpr}: Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
\item \code{ndcg}: Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
}
The following callbacks are automatically created when certain parameters are set:
\itemize{
\item \code{xgb.cb.print.evaluation} is turned on when \code{verbose > 0};
and the \code{print_every_n} parameter is passed to it.
\item \code{xgb.cb.evaluation.log} is on when \code{evals} is present.
\item \code{xgb.cb.early.stop}: when \code{early_stopping_rounds} is set.
\item \code{xgb.cb.save.model}: when \code{save_period > 0} is set.
\item \code{\link[=xgb.cb.print.evaluation]{xgb.cb.print.evaluation()}} is turned on when \code{verbose > 0} and the \code{print_every_n}
parameter is passed to it.
\item \code{\link[=xgb.cb.evaluation.log]{xgb.cb.evaluation.log()}} is on when \code{evals} is present.
\item \code{\link[=xgb.cb.early.stop]{xgb.cb.early.stop()}}: When \code{early_stopping_rounds} is set.
\item \code{\link[=xgb.cb.save.model]{xgb.cb.save.model()}}: When \code{save_period > 0} is set.
}
Note that objects of type \code{xgb.Booster} as returned by this function behave a bit differently
from typical R objects (it's an 'altrep' list class), and it makes a separation between
internal booster attributes (restricted to jsonifyable data), accessed through \link{xgb.attr}
and shared between interfaces through serialization functions like \link{xgb.save}; and
R-specific attributes (typically the result from a callback), accessed through \link{attributes}
and \link{attr}, which are otherwise
only used in the R interface, only kept when using R's serializers like \link{saveRDS}, and
not anyhow used by functions like \link{predict.xgb.Booster}.
internal booster attributes (restricted to jsonifyable data), accessed through \code{\link[=xgb.attr]{xgb.attr()}}
and shared between interfaces through serialization functions like \code{\link[=xgb.save]{xgb.save()}}; and
R-specific attributes (typically the result from a callback), accessed through \code{\link[=attributes]{attributes()}}
and \code{\link[=attr]{attr()}}, which are otherwise
only used in the R interface, only kept when using R's serializers like \code{\link[=saveRDS]{saveRDS()}}, and
not anyhow used by functions like \code{predict.xgb.Booster()}.
Be aware that one such R attribute that is automatically added is \code{params} - this attribute
is assigned from the \code{params} argument to this function, and is only meant to serve as a
reference for what went into the booster, but is not used in other methods that take a booster
object - so for example, changing the booster's configuration requires calling \verb{xgb.config<-}
or 'xgb.parameters<-', while simply modifying \verb{attributes(model)$params$<...>} will have no
or \verb{xgb.parameters<-}, while simply modifying \verb{attributes(model)$params$<...>} will have no
effect elsewhere.
}
\examples{
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
data(agaricus.train, package = "xgboost")
data(agaricus.test, package = "xgboost")
## Keep the number of threads to 1 for examples
nthread <- 1
@@ -308,8 +295,13 @@ dtest <- with(
evals <- list(train = dtrain, eval = dtest)
## A simple xgb.train example:
param <- list(max_depth = 2, eta = 1, nthread = nthread,
objective = "binary:logistic", eval_metric = "auc")
param <- list(
max_depth = 2,
eta = 1,
nthread = nthread,
objective = "binary:logistic",
eval_metric = "auc"
)
bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0)
## An xgb.train example where custom objective and evaluation metric are
@@ -329,34 +321,65 @@ evalerror <- function(preds, dtrain) {
# These functions could be used by passing them either:
# as 'objective' and 'eval_metric' parameters in the params list:
param <- list(max_depth = 2, eta = 1, nthread = nthread,
objective = logregobj, eval_metric = evalerror)
param <- list(
max_depth = 2,
eta = 1,
nthread = nthread,
objective = logregobj,
eval_metric = evalerror
)
bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0)
# or through the ... arguments:
param <- list(max_depth = 2, eta = 1, nthread = nthread)
bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
objective = logregobj, eval_metric = evalerror)
bst <- xgb.train(
param,
dtrain,
nrounds = 2,
evals = evals,
verbose = 0,
objective = logregobj,
eval_metric = evalerror
)
# or as dedicated 'obj' and 'feval' parameters of xgb.train:
bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals,
obj = logregobj, feval = evalerror)
bst <- xgb.train(
param, dtrain, nrounds = 2, evals = evals, obj = logregobj, feval = evalerror
)
## An xgb.train example of using variable learning rates at each iteration:
param <- list(max_depth = 2, eta = 1, nthread = nthread,
objective = "binary:logistic", eval_metric = "auc")
param <- list(
max_depth = 2,
eta = 1,
nthread = nthread,
objective = "binary:logistic",
eval_metric = "auc"
)
my_etas <- list(eta = c(0.5, 0.1))
bst <- xgb.train(param, dtrain, nrounds = 2, evals = evals, verbose = 0,
callbacks = list(xgb.cb.reset.parameters(my_etas)))
bst <- xgb.train(
param,
dtrain,
nrounds = 2,
evals = evals,
verbose = 0,
callbacks = list(xgb.cb.reset.parameters(my_etas))
)
## Early stopping:
bst <- xgb.train(param, dtrain, nrounds = 25, evals = evals,
early_stopping_rounds = 3)
bst <- xgb.train(
param, dtrain, nrounds = 25, evals = evals, early_stopping_rounds = 3
)
## An 'xgboost' interface example:
bst <- xgboost(x = agaricus.train$data, y = factor(agaricus.train$label),
params = list(max_depth = 2, eta = 1), nthread = nthread, nrounds = 2)
bst <- xgboost(
x = agaricus.train$data,
y = factor(agaricus.train$label),
params = list(max_depth = 2, eta = 1),
nthread = nthread,
nrounds = 2
)
pred <- predict(bst, agaricus.test$data)
}
@@ -365,7 +388,5 @@ Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, \url{https://arxiv.org/abs/1603.02754}
}
\seealso{
\code{\link{xgb.Callback}},
\code{\link{predict.xgb.Booster}},
\code{\link{xgb.cv}}
\code{\link[=xgb.Callback]{xgb.Callback()}}, \code{\link[=predict.xgb.Booster]{predict.xgb.Booster()}}, \code{\link[=xgb.cv]{xgb.cv()}}
}

8
R-package/man/xgbConfig.Rd
View File

@@ -14,21 +14,21 @@ xgb.get.config()
\item{...}{List of parameters to be set, as keyword arguments}
}
\value{
\code{xgb.set.config} returns \code{TRUE} to signal success. \code{xgb.get.config} returns
\code{xgb.set.config()} returns \code{TRUE} to signal success. \code{xgb.get.config()} returns
a list containing all global-scope parameters and their values.
}
\description{
Global configuration consists of a collection of parameters that can be applied in the global
scope. See \url{https://xgboost.readthedocs.io/en/stable/parameter.html} for the full list of
parameters supported in the global configuration. Use \code{xgb.set.config} to update the
values of one or more global-scope parameters. Use \code{xgb.get.config} to fetch the current
parameters supported in the global configuration. Use \code{xgb.set.config()} to update the
values of one or more global-scope parameters. Use \code{xgb.get.config()} to fetch the current
values of all global-scope parameters (listed in
\url{https://xgboost.readthedocs.io/en/stable/parameter.html}).
}
\details{
Note that serialization-related functions might use a globally-configured number of threads,
which is managed by the system's OpenMP (OMP) configuration instead. Typically, XGBoost methods
accept an \code{nthreads} parameter, but some methods like \code{readRDS} might get executed before such
accept an \code{nthreads} parameter, but some methods like \code{\link[=readRDS]{readRDS()}} might get executed before such
parameter can be supplied.
The number of OMP threads can in turn be configured for example through an environment variable

148
R-package/man/xgboost.Rd
View File

@@ -21,65 +21,69 @@ xgboost(
)
}
\arguments{
\item{x}{The features / covariates. Can be passed as:\itemize{
\item A numeric or integer `matrix`.
\item A `data.frame`, in which all columns are one of the following types:\itemize{
\item `numeric`
\item `integer`
\item `logical`
\item `factor`
\item{x}{The features / covariates. Can be passed as:
\itemize{
\item A numeric or integer \code{matrix}.
\item A \code{data.frame}, in which all columns are one of the following types:
\itemize{
\item \code{numeric}
\item \code{integer}
\item \code{logical}
\item \code{factor}
}
Columns of `factor` type will be assumed to be categorical, while other column types will
Columns of \code{factor} type will be assumed to be categorical, while other column types will
be assumed to be numeric.
\item A sparse matrix from the `Matrix` package, either as `dgCMatrix` or `dgRMatrix` class.
\item A sparse matrix from the \code{Matrix} package, either as \code{dgCMatrix} or \code{dgRMatrix} class.
}
Note that categorical features are only supported for `data.frame` inputs, and are automatically
determined based on their types. See \link{xgb.train} with \link{xgb.DMatrix} for more flexible
Note that categorical features are only supported for \code{data.frame} inputs, and are automatically
determined based on their types. See \code{\link[=xgb.train]{xgb.train()}} with \code{\link[=xgb.DMatrix]{xgb.DMatrix()}} for more flexible
variants that would allow something like categorical features on sparse matrices.}
\item{y}{The response variable. Allowed values are:\itemize{
\item{y}{The response variable. Allowed values are:
\itemize{
\item A numeric or integer vector (for regression tasks).
\item A factor or character vector (for binary and multi-class classification tasks).
\item A logical (boolean) vector (for binary classification tasks).
\item A numeric or integer matrix or `data.frame` with numeric/integer columns
\item A numeric or integer matrix or \code{data.frame} with numeric/integer columns
(for multi-task regression tasks).
\item A `Surv` object from the `survival` package (for survival tasks).
\item A \code{Surv} object from the 'survival' package (for survival tasks).
}
If `objective` is `NULL`, the right task will be determined automatically based on
the class of `y`.
If \code{objective} is \code{NULL}, the right task will be determined automatically based on
the class of \code{y}.
If `objective` is not `NULL`, it must match with the type of `y` - e.g. `factor` types of `y`
If \code{objective} is not \code{NULL}, it must match with the type of \code{y} - e.g. \code{factor} types of \code{y}
can only be used with classification objectives and vice-versa.
For binary classification, the last factor level of `y` will be used as the "positive"
class - that is, the numbers from `predict` will reflect the probabilities of belonging to this
class instead of to the first factor level. If `y` is a `logical` vector, then `TRUE` will be
For binary classification, the last factor level of \code{y} will be used as the "positive"
class - that is, the numbers from \code{predict} will reflect the probabilities of belonging to this
class instead of to the first factor level. If \code{y} is a \code{logical} vector, then \code{TRUE} will be
set as the last level.}
\item{objective}{Optimization objective to minimize based on the supplied data, to be passed
by name as a string / character (e.g. `reg:absoluteerror`). See the
\href{https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters}{
Learning Task Parameters} page for more detailed information on allowed values.
by name as a string / character (e.g. \code{reg:absoluteerror}). See the
\href{https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters}{Learning Task Parameters}
page for more detailed information on allowed values.
If `NULL` (the default), will be automatically determined from `y` according to the following
logic:\itemize{
\item If `y` is a factor with 2 levels, will use `binary:logistic`.
\item If `y` is a factor with more than 2 levels, will use `multi:softprob` (number of classes
will be determined automatically, should not be passed under `params`).
\item If `y` is a `Surv` object from the `survival` package, will use `survival:aft` (note that
If \code{NULL} (the default), will be automatically determined from \code{y} according to the following
logic:
\itemize{
\item If \code{y} is a factor with 2 levels, will use \code{binary:logistic}.
\item If \code{y} is a factor with more than 2 levels, will use \code{multi:softprob} (number of classes
will be determined automatically, should not be passed under \code{params}).
\item If \code{y} is a \code{Surv} object from the \code{survival} package, will use \code{survival:aft} (note that
the only types supported are left / right / interval censored).
\item Otherwise, will use `reg:squarederror`.
\item Otherwise, will use \code{reg:squarederror}.
}
If `objective` is not `NULL`, it must match with the type of `y` - e.g. `factor` types of `y`
If \code{objective} is not \code{NULL}, it must match with the type of \code{y} - e.g. \code{factor} types of \code{y}
can only be used with classification objectives and vice-versa.
Note that not all possible `objective` values supported by the core XGBoost library are allowed
Note that not all possible \code{objective} values supported by the core XGBoost library are allowed
here - for example, objectives which are a variation of another but with a different default
prediction type (e.g. `multi:softmax` vs. `multi:softprob`) are not allowed, and neither are
prediction type (e.g. \code{multi:softmax} vs. \code{multi:softprob}) are not allowed, and neither are
ranking objectives, nor custom objectives at the moment.}
\item{nrounds}{Number of boosting iterations / rounds.
@@ -87,56 +91,54 @@ ranking objectives, nor custom objectives at the moment.}
Note that the number of default boosting rounds here is not automatically tuned, and different
problems will have vastly different optimal numbers of boosting rounds.}
\item{weights}{Sample weights for each row in `x` and `y`. If `NULL` (the default), each row
\item{weights}{Sample weights for each row in \code{x} and \code{y}. If \code{NULL} (the default), each row
will have the same weight.
If not `NULL`, should be passed as a numeric vector with length matching to the number of
rows in `x`.}
If not \code{NULL}, should be passed as a numeric vector with length matching to the number of rows in \code{x}.}
\item{verbosity}{Verbosity of printing messages. Valid values of 0 (silent), 1 (warning),
2 (info), and 3 (debug).}
\item{nthreads}{Number of parallel threads to use. If passing zero, will use all CPU threads.}
\item{seed}{Seed to use for random number generation. If passing `NULL`, will draw a random
\item{seed}{Seed to use for random number generation. If passing \code{NULL}, will draw a random
number using R's PRNG system to use as seed.}
\item{monotone_constraints}{Optional monotonicity constraints for features.
Can be passed either as a named list (when `x` has column names), or as a vector. If passed
as a vector and `x` has column names, will try to match the elements by name.
Can be passed either as a named list (when \code{x} has column names), or as a vector. If passed
as a vector and \code{x} has column names, will try to match the elements by name.
A value of `+1` for a given feature makes the model predictions / scores constrained to be
A value of \code{+1} for a given feature makes the model predictions / scores constrained to be
a monotonically increasing function of that feature (that is, as the value of the feature
increases, the model prediction cannot decrease), while a value of `-1` makes it a monotonically
increases, the model prediction cannot decrease), while a value of \code{-1} makes it a monotonically
decreasing function. A value of zero imposes no constraint.
The input for `monotone_constraints` can be a subset of the columns of `x` if named, in which
case the columns that are not referred to in `monotone_constraints` will be assumed to have
The input for \code{monotone_constraints} can be a subset of the columns of \code{x} if named, in which
case the columns that are not referred to in \code{monotone_constraints} will be assumed to have
a value of zero (no constraint imposed on the model for those features).
See the tutorial \href{https://xgboost.readthedocs.io/en/stable/tutorials/monotonic.html}{
Monotonic Constraints} for a more detailed explanation.}
See the tutorial \href{https://xgboost.readthedocs.io/en/stable/tutorials/monotonic.html}{Monotonic Constraints}
for a more detailed explanation.}
\item{interaction_constraints}{Constraints for interaction representing permitted interactions.
The constraints must be specified in the form of a list of vectors referencing columns in the
data, e.g. `list(c(1, 2), c(3, 4, 5))` (with these numbers being column indices, numeration
data, e.g. \code{list(c(1, 2), c(3, 4, 5))} (with these numbers being column indices, numeration
starting at 1 - i.e. the first sublist references the first and second columns) or
`list(c("Sepal.Length", "Sepal.Width"), c("Petal.Length", "Petal.Width"))` (references
\code{list(c("Sepal.Length", "Sepal.Width"), c("Petal.Length", "Petal.Width"))} (references
columns by names), where each vector is a group of indices of features that are allowed to
interact with each other.
See the tutorial
\href{https://xgboost.readthedocs.io/en/stable/tutorials/feature_interaction_constraint.html}{
Feature Interaction Constraints} for more information.}
See the tutorial \href{https://xgboost.readthedocs.io/en/stable/tutorials/feature_interaction_constraint.html}{Feature Interaction Constraints}
for more information.}
\item{feature_weights}{Feature weights for column sampling.
Can be passed either as a vector with length matching to columns of `x`, or as a named
list (only if `x` has column names) with names matching to columns of 'x'. If it is a
named vector, will try to match the entries to column names of `x` by name.
Can be passed either as a vector with length matching to columns of \code{x}, or as a named
list (only if \code{x} has column names) with names matching to columns of 'x'. If it is a
named vector, will try to match the entries to column names of \code{x} by name.
If `NULL` (the default), all columns will have the same weight.}
If \code{NULL} (the default), all columns will have the same weight.}
\item{base_margin}{Base margin used for boosting from existing model.
@@ -145,53 +147,53 @@ here - for example, one can pass the raw scores from a previous model, or some p
offset, or similar.
Should be either a numeric vector or numeric matrix (for multi-class and multi-target objectives)
with the same number of rows as `x` and number of columns corresponding to number of optimization
targets, and should be in the untransformed scale (for example, for objective `binary:logistic`,
it should have log-odds, not probabilities; and for objective `multi:softprob`, should have
with the same number of rows as \code{x} and number of columns corresponding to number of optimization
targets, and should be in the untransformed scale (for example, for objective \code{binary:logistic},
it should have log-odds, not probabilities; and for objective \code{multi:softprob}, should have
number of columns matching to number of classes in the data).
Note that, if it contains more than one column, then columns will not be matched by name to
the corresponding `y` - `base_margin` should have the same column order that the model will use
(for example, for objective `multi:softprob`, columns of `base_margin` will be matched against
`levels(y)` by their position, regardless of what `colnames(base_margin)` returns).
the corresponding \code{y} - \code{base_margin} should have the same column order that the model will use
(for example, for objective \code{multi:softprob}, columns of \code{base_margin} will be matched against
\code{levels(y)} by their position, regardless of what \code{colnames(base_margin)} returns).
If `NULL`, will start from zero, but note that for most objectives, an intercept is usually
added (controllable through parameter `base_score` instead) when `base_margin` is not passed.}
If \code{NULL}, will start from zero, but note that for most objectives, an intercept is usually
added (controllable through parameter \code{base_score} instead) when \code{base_margin} is not passed.}
\item{...}{Other training parameters. See the online documentation
\href{https://xgboost.readthedocs.io/en/stable/parameter.html}{XGBoost Parameters} for
details about possible values and what they do.
Note that not all possible values from the core XGBoost library are allowed as `params` for
Note that not all possible values from the core XGBoost library are allowed as \code{params} for
'xgboost()' - in particular, values which require an already-fitted booster object (such as
`process_type`) are not accepted here.}
\code{process_type}) are not accepted here.}
}
\value{
A model object, inheriting from both `xgboost` and `xgb.Booster`. Compared to the regular
`xgb.Booster` model class produced by \link{xgb.train}, this `xgboost` class will have an
additional attribute `metadata` containing information which is used for formatting prediction
A model object, inheriting from both \code{xgboost} and \code{xgb.Booster}. Compared to the regular
\code{xgb.Booster} model class produced by \code{\link[=xgb.train]{xgb.train()}}, this \code{xgboost} class will have an
additional attribute \code{metadata} containing information which is used for formatting prediction
outputs, such as class names for classification problems.
}
\description{
Fits an XGBoost model (boosted decision tree ensemble) to given x/y data.
See the tutorial \href{https://xgboost.readthedocs.io/en/stable/tutorials/model.html}{
Introduction to Boosted Trees} for a longer explanation of what XGBoost does.
See the tutorial \href{https://xgboost.readthedocs.io/en/stable/tutorials/model.html}{Introduction to Boosted Trees}
for a longer explanation of what XGBoost does.
This function is intended to provide a more user-friendly interface for XGBoost that follows
R's conventions for model fitting and predictions, but which doesn't expose all of the
possible functionalities of the core XGBoost library.
See \link{xgb.train} for a more flexible low-level alternative which is similar across different
See \code{\link[=xgb.train]{xgb.train()}} for a more flexible low-level alternative which is similar across different
language bindings of XGBoost and which exposes the full library's functionalities.
}
\details{
For package authors using `xgboost` as a dependency, it is highly recommended to use
\link{xgb.train} in package code instead of `xgboost()`, since it has a more stable interface
For package authors using 'xgboost' as a dependency, it is highly recommended to use
\code{\link[=xgb.train]{xgb.train()}} in package code instead of \code{\link[=xgboost]{xgboost()}}, since it has a more stable interface
and performs fewer data conversions and copies along the way.
}
\examples{
library(xgboost)
data(mtcars)
# Fit a small regression model on the mtcars data