#' Tidying methods for Spark ML Naive Bayes
#'
#' 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_naive_bayes_tidiers
NULL
#' @rdname ml_naive_bayes_tidiers
#' @export
tidy.ml_model_naive_bayes <- function(x,
...) {
theta <- fix_data_frame(x$theta) %>%
dplyr::rename(.label = !!"term")
pi <- as.data.frame(x$pi) %>%
dplyr::rename(.pi = !!"x$pi")
res <- dplyr::bind_cols(theta, pi)
res <- as.data.frame(res)
rownames(res) <- seq(nrow(res))
res
}
#' @rdname ml_naive_bayes_tidiers
#' @param newdata a tbl_spark of new data to use for prediction.
#'
#' @export
augment.ml_model_naive_bayes <- function(x, newdata = NULL,
...) {
broom_augment_supervised(x, newdata = newdata)
}
#' @rdname ml_naive_bayes_tidiers
#' @export
glance.ml_model_naive_bayes <- function(x, ...) {
model_type <- x$model$param_map$model_type
smoothing <- x$model$param_map$smoothing
dplyr::tibble(
model_type = model_type,
smoothing = smoothing
)
}
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