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[R] Remove enable_categorical parameter (#10018)

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david-cortes
2024-01-30 22:17:36 +01:00
committed by GitHub
parent 3abbbe41ac
commit df7cf744b4
4 changed files with 15 additions and 79 deletions
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46
R-package/R/xgb.DMatrix.R
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@@ -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)