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[doc] Clarify the effect of enable_categorical (#9877)

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david-cortes 2023-12-13 01:39:41 +01:00 committed by GitHub
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3 changed files with 36 additions and 6 deletions
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14
R-package/R/xgb.DMatrix.R
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@ -27,14 +27,24 @@
#' @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.
#'
#' @details
#' Note that DMatrix objects are not serializable through R functions such as \code{saveRDS} or \code{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.
#' @param enable_categorical Experimental support of specializing for
#' categorical features. JSON/UBJSON serialization format is required.
#'
#' @examples
#' data(agaricus.train, package='xgboost')

14
R-package/man/xgb.DMatrix.Rd
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@ -58,8 +58,18 @@ frame and matrix.}
\item{feature_weights}{Set feature weights for column sampling.}
\item{enable_categorical}{Experimental support of specializing for
categorical features. JSON/UBJSON serialization format is required.}
\item{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.}
}
\description{
Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file.

14
python-package/xgboost/core.py
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@ -822,8 +822,18 @@ class DMatrix: # pylint: disable=too-many-instance-attributes,too-many-public-m
.. note:: This parameter is experimental
Experimental support of specializing for categorical features. JSON/UBJSON
serialization format is required.
Experimental support of specializing for categorical features.
If passing 'True' and 'data' is a data frame (from supported libraries
such as Pandas or Modin), 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 group is not None and qid is not None: