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#' Create a Dataset from LibSVM files.
#'
#' @param file_names A `tf.string` tensor containing one or more filenames.
#' @param num_features The number of features.
#' @param dtype The type of the output feature tensor. Default to `tf.float32`.
#' @param label_dtype The type of the output label tensor. Default to
#' `tf.int64`.
#' @param batch_size An integer representing the number of records to combine in
#' a single batch, default 1.
#' @param compression_type A `tf.string` scalar evaluating to one of `""` (no
#' compression), `"ZLIB"`, or `"GZIP"`.
#' @param buffer_size A `tf.int64` scalar denoting the number of bytes to
#' buffer. A value of 0 results in the default buffering values chosen based
#' on the compression type.
#' @param num_parallel_parser_calls Number of parallel records to parse in
#' parallel. Defaults to an automatic selection.
#' @param drop_final_batch Whether the last batch should be dropped in case its
#' size is smaller than `batch_size`; the default behavior is not to drop the
#' smaller batch.
#' @param prefetch_buffer_size An integer specifying the number of feature
#' batches to prefetch for performance improvement. Defaults to auto-tune. Set
#' to 0 to disable prefetching.
#'
#' @export
make_libsvm_dataset <- function(
file_names,
num_features,
dtype = NULL,
label_dtype = NULL,
batch_size = 1,
compression_type = '',
buffer_size = NULL,
num_parallel_parser_calls = NULL,
drop_final_batch = FALSE,
prefetch_buffer_size = 0) {
dataset <- tfio_lib$libsvm$make_libsvm_dataset(
file_names = file_names,
num_features = num_features,
dtype = dtype,
label_dtype = label_dtype,
batch_size = cast_scalar_integer(batch_size),
compression_type = compression_type,
buffer_size = cast_nullable_scalar_integer(buffer_size),
num_parallel_parser_calls = cast_nullable_scalar_integer(num_parallel_parser_calls),
drop_final_batch = cast_logical(drop_final_batch),
prefetch_buffer_size = cast_scalar_integer(prefetch_buffer_size)
)
as_tf_dataset(dataset)
}
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