R/tidiers_pca.R

Defines functions glance.ml_model_pca augment.ml_model_pca tidy.ml_model_pca

Documented in augment.ml_model_pca glance.ml_model_pca tidy.ml_model_pca

#' 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()
}

Try the sparklyr package in your browser

Any scripts or data that you put into this service are public.

sparklyr documentation built on Jan. 8, 2022, 5:06 p.m.