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#' Tidying methods for Spark ML ALS
#'
#' These methods summarize the results of Spark ML models into tidy forms.
#'
#' @param x a Spark ML model.
#' @param ... extra arguments (not used.)
#' @name ml_als_tidiers
NULL
#' @rdname ml_als_tidiers
#' @export
tidy.ml_model_als <- function(x, ...) {
user_factors <- x$model$user_factors %>%
dplyr::select(!!"id", !!"features") %>%
dplyr::rename(user_factors = !!"features")
item_factors <- x$model$item_factors %>%
dplyr::select(!!"id", !!"features") %>%
dplyr::rename(item_factors = !!"features")
dplyr::full_join(user_factors, item_factors, by = "id")
}
#' @rdname ml_als_tidiers
#' @param newdata a tbl_spark of new data to use for prediction.
#'
#' @export
augment.ml_model_als <- function(x, newdata = NULL, ...) {
# if the user doesn't provide a new data, this funcion will
# use the training set
if (is.null(newdata)) {
newdata <- x$dataset
}
broom_augment_supervised(x, newdata = newdata)
}
#' @rdname ml_als_tidiers
#' @export
glance.ml_model_als <- function(x, ...) {
rank <- x$model$rank
cold_start_strategy <- x$model$param_map$cold_start_strategy
dplyr::tibble(
rank = rank,
cold_start_strategy = cold_start_strategy
)
}
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