diff --git a/R-package/R/xgb.DMatrix.R b/R-package/R/xgb.DMatrix.R
index da036b952..58ba34050 100644
--- a/R-package/R/xgb.DMatrix.R
+++ b/R-package/R/xgb.DMatrix.R
@@ -16,10 +16,6 @@
#' \item `matrix` objects, with types `numeric`, `integer`, or `logical`.
#' \item `data.frame` objects, with columns of types `numeric`, `integer`, `logical`, or `factor`.
#'
-#' If passing `enable_categorical=TRUE`, columns with `factor` type will be treated as categorical.
-#' Otherwise, if passing `enable_categorical=FALSE` and the data contains `factor` columns, an error
-#' will be thrown.
-#'
#' Note that xgboost uses base-0 encoding for categorical types, hence `factor` types (which use base-1
#' encoding') will be converted inside the function call. Be aware that the encoding used for `factor`
#' types is not kept as part of the model, so in subsequent calls to `predict`, it is the user's
@@ -59,7 +55,7 @@
#' must be the same as in the DMatrix construction, regardless of the column names.
#' @param feature_types Set types for features.
#'
-#' If `data` is a `data.frame` and passing `enable_categorical=TRUE`, the types will be deduced
+#' If `data` is a `data.frame` and passing `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 `data`,
@@ -79,18 +75,6 @@
#' @param label_lower_bound Lower bound for survival training.
#' @param label_upper_bound Upper bound for survival training.
#' @param feature_weights Set feature weights for column sampling.
-#' @param enable_categorical Experimental support of specializing for categorical features.
-#'
-#' If passing 'TRUE' and 'data' is a data frame,
-#' columns of categorical types will automatically
-#' be set to be of categorical type (feature_type='c') in the resulting DMatrix.
-#'
-#' If passing 'FALSE' and 'data' is a data frame with categorical columns,
-#' it will result in an error being thrown.
-#'
-#' If 'data' is not a data frame, this argument is ignored.
-#'
-#' JSON/UBJSON serialization format is required for this.
#' @return An 'xgb.DMatrix' object. If calling 'xgb.QuantileDMatrix', it will have additional
#' subclass 'xgb.QuantileDMatrix'.
#'
@@ -127,8 +111,7 @@ xgb.DMatrix <- function(
qid = NULL,
label_lower_bound = NULL,
label_upper_bound = NULL,
- feature_weights = NULL,
- enable_categorical = FALSE
+ feature_weights = NULL
) {
if (!is.null(group) && !is.null(qid)) {
stop("Either one of 'group' or 'qid' should be NULL")
@@ -180,7 +163,7 @@ xgb.DMatrix <- function(
nthread
)
} else if (is.data.frame(data)) {
- tmp <- .process.df.for.dmatrix(data, enable_categorical, feature_types)
+ tmp <- .process.df.for.dmatrix(data, feature_types)
feature_types <- tmp$feature_types
handle <- .Call(
XGDMatrixCreateFromDF_R, tmp$lst, missing, nthread
@@ -212,7 +195,7 @@ xgb.DMatrix <- function(
return(dmat)
}
-.process.df.for.dmatrix <- function(df, enable_categorical, feature_types) {
+.process.df.for.dmatrix <- function(df, feature_types) {
if (!nrow(df) || !ncol(df)) {
stop("'data' is an empty data.frame.")
}
@@ -225,12 +208,6 @@ xgb.DMatrix <- function(
} else {
feature_types <- sapply(df, function(col) {
if (is.factor(col)) {
- if (!enable_categorical) {
- stop(
- "When factor type is used, the parameter `enable_categorical`",
- " must be set to TRUE."
- )
- }
return("c")
} else if (is.integer(col)) {
return("int")
@@ -326,7 +303,6 @@ xgb.QuantileDMatrix <- function(
label_lower_bound = NULL,
label_upper_bound = NULL,
feature_weights = NULL,
- enable_categorical = FALSE,
ref = NULL,
max_bin = NULL
) {
@@ -357,8 +333,7 @@ xgb.QuantileDMatrix <- function(
qid = qid,
label_lower_bound = label_lower_bound,
label_upper_bound = label_upper_bound,
- feature_weights = feature_weights,
- enable_categorical = enable_categorical
+ feature_weights = feature_weights
)
)
data_iterator <- .single.data.iterator(iterator_env)
@@ -470,8 +445,7 @@ xgb.DataIter <- function(env = new.env(), f_next, f_reset) {
qid = env[["qid"]],
label_lower_bound = env[["label_lower_bound"]],
label_upper_bound = env[["label_upper_bound"]],
- feature_weights = env[["feature_weights"]],
- enable_categorical = env[["enable_categorical"]]
+ feature_weights = env[["feature_weights"]]
)
)
}
@@ -540,8 +514,7 @@ xgb.ProxyDMatrix <- function(
qid = NULL,
label_lower_bound = NULL,
label_upper_bound = NULL,
- feature_weights = NULL,
- enable_categorical = FALSE
+ feature_weights = NULL
) {
stopifnot(inherits(data, c("matrix", "data.frame", "dgRMatrix")))
out <- list(
@@ -555,8 +528,7 @@ xgb.ProxyDMatrix <- function(
qid = qid,
label_lower_bound = label_lower_bound,
label_upper_bound = label_upper_bound,
- feature_weights = feature_weights,
- enable_categorical = enable_categorical
+ feature_weights = feature_weights
)
class(out) <- "xgb.ProxyDMatrix"
return(out)
@@ -575,7 +547,7 @@ xgb.ProxyDMatrix.internal <- function(proxy_handle, data_iterator) {
stop("Either one of 'group' or 'qid' should be NULL")
}
if (is.data.frame(lst$data)) {
- tmp <- .process.df.for.dmatrix(lst$data, lst$enable_categorical, lst$feature_types)
+ tmp <- .process.df.for.dmatrix(lst$data, lst$feature_types)
lst$feature_types <- tmp$feature_types
.Call(XGProxyDMatrixSetDataColumnar_R, proxy_handle, tmp$lst)
rm(tmp)
diff --git a/R-package/man/xgb.DMatrix.Rd b/R-package/man/xgb.DMatrix.Rd
index ceb60dc42..8e108a2fa 100644
--- a/R-package/man/xgb.DMatrix.Rd
+++ b/R-package/man/xgb.DMatrix.Rd
@@ -19,8 +19,7 @@ xgb.DMatrix(
qid = NULL,
label_lower_bound = NULL,
label_upper_bound = NULL,
- feature_weights = NULL,
- enable_categorical = FALSE
+ feature_weights = NULL
)
xgb.QuantileDMatrix(
@@ -37,7 +36,6 @@ xgb.QuantileDMatrix(
label_lower_bound = NULL,
label_upper_bound = NULL,
feature_weights = NULL,
- enable_categorical = FALSE,
ref = NULL,
max_bin = NULL
)
@@ -50,10 +48,6 @@ Supported input types are as follows:\itemize{
\item \code{matrix} objects, with types \code{numeric}, \code{integer}, or \code{logical}.
\item \code{data.frame} objects, with columns of types \code{numeric}, \code{integer}, \code{logical}, or \code{factor}.
-If passing \code{enable_categorical=TRUE}, columns with \code{factor} type will be treated as categorical.
-Otherwise, if passing \code{enable_categorical=FALSE} and the data contains \code{factor} columns, an error
-will be thrown.
-
Note that xgboost uses base-0 encoding for categorical types, hence \code{factor} types (which use base-1
encoding') will be converted inside the function call. Be aware that the encoding used for \code{factor}
types is not kept as part of the model, so in subsequent calls to \code{predict}, it is the user's
@@ -102,7 +96,7 @@ frame and matrix.
\item{feature_types}{Set types for features.
-If \code{data} is a \code{data.frame} and passing \code{enable_categorical=TRUE}, the types will be deduced
+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},
@@ -129,20 +123,6 @@ functionalities such as feature importances.}
\item{feature_weights}{Set feature weights for column sampling.}
-\item{enable_categorical}{Experimental support of specializing for categorical features.
-
-\if{html}{\out{}}\preformatted{ If passing 'TRUE' and 'data' is a data frame,
- columns of categorical types will automatically
- be set to be of categorical type (feature_type='c') in the resulting DMatrix.
-
- If passing 'FALSE' and 'data' is a data frame with categorical columns,
- it will result in an error being thrown.
-
- If 'data' is not a data frame, this argument is ignored.
-
- JSON/UBJSON serialization format is required for this.
-}\if{html}{\out{
}}}
-
\item{ref}{The training dataset that provides quantile information, needed when creating
validation/test dataset with \code{xgb.QuantileDMatrix}. Supplying the training DMatrix
as a reference means that the same quantisation applied to the training data is
diff --git a/R-package/man/xgb.ProxyDMatrix.Rd b/R-package/man/xgb.ProxyDMatrix.Rd
index 5a9b6251a..cf173a2db 100644
--- a/R-package/man/xgb.ProxyDMatrix.Rd
+++ b/R-package/man/xgb.ProxyDMatrix.Rd
@@ -15,8 +15,7 @@ xgb.ProxyDMatrix(
qid = NULL,
label_lower_bound = NULL,
label_upper_bound = NULL,
- feature_weights = NULL,
- enable_categorical = FALSE
+ feature_weights = NULL
)
}
\arguments{
@@ -57,7 +56,7 @@ frame and matrix.
\item{feature_types}{Set types for features.
-If \code{data} is a \code{data.frame} and passing \code{enable_categorical=TRUE}, the types will be deduced
+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},
@@ -81,20 +80,6 @@ functionalities such as feature importances.}
\item{label_upper_bound}{Upper bound for survival training.}
\item{feature_weights}{Set feature weights for column sampling.}
-
-\item{enable_categorical}{Experimental support of specializing for categorical features.
-
-\if{html}{\out{}}\preformatted{ If passing 'TRUE' and 'data' is a data frame,
- columns of categorical types will automatically
- be set to be of categorical type (feature_type='c') in the resulting DMatrix.
-
- If passing 'FALSE' and 'data' is a data frame with categorical columns,
- it will result in an error being thrown.
-
- If 'data' is not a data frame, this argument is ignored.
-
- JSON/UBJSON serialization format is required for this.
-}\if{html}{\out{
}}}
}
\value{
An object of class \code{xgb.ProxyDMatrix}, which is just a list containing the
diff --git a/R-package/tests/testthat/test_dmatrix.R b/R-package/tests/testthat/test_dmatrix.R
index 65374240d..cd871c8c8 100644
--- a/R-package/tests/testthat/test_dmatrix.R
+++ b/R-package/tests/testthat/test_dmatrix.R
@@ -338,19 +338,18 @@ test_that("xgb.DMatrix: data.frame", {
stringsAsFactors = TRUE
)
- m <- xgb.DMatrix(df, enable_categorical = TRUE)
+ m <- xgb.DMatrix(df)
expect_equal(colnames(m), colnames(df))
expect_equal(
getinfo(m, "feature_type"), c("float", "float", "int", "i", "c", "c")
)
- expect_error(xgb.DMatrix(df, enable_categorical = FALSE))
df <- data.frame(
missing = c("a", "b", "d", NA),
valid = c("a", "b", "d", "c"),
stringsAsFactors = TRUE
)
- m <- xgb.DMatrix(df, enable_categorical = TRUE)
+ m <- xgb.DMatrix(df)
expect_equal(getinfo(m, "feature_type"), c("c", "c"))
})