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[R] Remove enable_categorical parameter (#10018)

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This commit is contained in:
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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24
R-package/man/xgb.DMatrix.Rd
View File

@@ -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{<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

19
R-package/man/xgb.ProxyDMatrix.Rd
View File

@@ -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{<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