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#' Tidying methods for Spark ML Principal Component Analysis
#'
#' 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_pca_tidiers
NULL
#' @rdname ml_pca_tidiers
#' @export
tidy.ml_model_pca <- function(x, ...) {
dplyr::as_tibble(x$pc, rownames = "features")
}
#' @rdname ml_pca_tidiers
#' @param newdata a tbl_spark of new data to use for prediction.
#'
#' @export
augment.ml_model_pca <- function(x, newdata = NULL,
...) {
# if the user doesn't provide a new data, this funcion will
# use the training set
if (is.null(newdata)) {
newdata <- x$dataset
}
sdf_project(x, newdata)
}
#' @rdname ml_pca_tidiers
#' @export
glance.ml_model_pca <- function(x, ...) {
explained_variance <- x$explained_variance
names(explained_variance) <- purrr::map_chr(
names(explained_variance),
function(e) paste0("explained_variance_", e)
)
k <- c("k" = x$k)
c(k, explained_variance) %>%
as.list() %>%
dplyr::as_tibble()
}
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