[R] Remove enable_categorical parameter (#10018)

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,
+  feature_weights = NULL
-  enable_categorical = FALSE
 ) {
   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,
+      feature_weights = feature_weights
-      enable_categorical = enable_categorical
     )
   )
   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"]],
+      feature_weights = env[["feature_weights"]]
-      enable_categorical = env[["enable_categorical"]]
     )
   )
 }
@@ -540,8 +514,7 @@ xgb.ProxyDMatrix <- function(
   qid = NULL,
   label_lower_bound = NULL,
   label_upper_bound = NULL,
-  feature_weights = NULL,
+  feature_weights = NULL
-  enable_categorical = FALSE
 ) {
   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,
+    feature_weights = feature_weights
-    enable_categorical = enable_categorical
   )
   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)

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,
+  feature_weights = NULL
-  enable_categorical = FALSE
 )
 
 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{<div class="sourceCode">}}\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{</div>}}}
-
 \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

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,
+  feature_weights = NULL
-  enable_categorical = FALSE
 )
 }
 \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{<div class="sourceCode">}}\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{</div>}}}
 }
 \value{
 An object of class \code{xgb.ProxyDMatrix}, which is just a list containing the

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"))
 })