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#' @rdname ft_lsh
#' @export
ft_minhash_lsh <- function(x, input_col = NULL, output_col = NULL,
num_hash_tables = 1L, seed = NULL,
uid = random_string("minhash_lsh_"), ...) {
check_dots_used()
UseMethod("ft_minhash_lsh")
}
ml_minhash_lsh <- ft_minhash_lsh
#' @export
ft_minhash_lsh.spark_connection <- function(x, input_col = NULL, output_col = NULL,
num_hash_tables = 1L, seed = NULL,
uid = random_string("minhash_lsh_"), ...) {
spark_require_version(x, "2.1.0", "MinHashLSH")
.args <- list(
input_col = input_col,
output_col = output_col,
num_hash_tables = num_hash_tables,
seed = seed,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_minhash_lsh()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.MinHashLSH",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
) %>%
invoke("setNumHashTables", .args[["num_hash_tables"]]) %>%
jobj_set_param("setSeed", .args[["seed"]])
estimator <- new_ml_minhash_lsh(jobj)
estimator
}
#' @export
ft_minhash_lsh.ml_pipeline <- function(x, input_col = NULL, output_col = NULL,
num_hash_tables = 1L, seed = NULL,
uid = random_string("minhash_lsh_"), ...) {
stage <- ft_minhash_lsh.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
num_hash_tables = num_hash_tables,
seed = seed,
uid = uid,
...
)
ml_add_stage(x, stage)
}
#' @export
ft_minhash_lsh.tbl_spark <- function(x, input_col = NULL, output_col = NULL,
num_hash_tables = 1L, seed = NULL,
uid = random_string("minhash_lsh_"), ...) {
stage <- ft_minhash_lsh.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
num_hash_tables = num_hash_tables,
seed = seed,
uid = uid,
...
)
if (is_ml_transformer(stage)) {
ml_transform(stage, x)
} else {
ml_fit_and_transform(stage, x)
}
}
new_ml_minhash_lsh <- function(jobj) {
new_ml_estimator(jobj, class = "ml_minhash_lsh")
}
new_ml_minhash_lsh_model <- function(jobj) {
new_ml_transformer(
jobj,
approx_nearest_neighbors = make_approx_nearest_neighbors(jobj),
approx_similarity_join = make_approx_similarity_join(jobj),
class = "ml_minhash_lsh_model"
)
}
validator_ml_minhash_lsh <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["num_hash_tables"]] <- cast_scalar_integer(.args[["num_hash_tables"]])
.args[["seed"]] <- cast_nullable_scalar_integer(.args[["seed"]])
.args
}
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