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#' Turn an dbscan clustering object into a tidy tibble
#'
#' Provides [tidy()][generics::tidy()], [augment()][generics::augment()], and
#' [glance()][generics::glance()] verbs for clusterings created with algorithms
#' in package `dbscan` to work with [tidymodels](https://www.tidymodels.org/).
#'
#' @param x An `dbscan` object returned from [dbscan::dbscan()].
#' @param data The data used to create the clustering.
#' @param newdata New data to predict cluster labels for.
#' @param ... further arguments are ignored without a warning.
#'
#' @name dbscan_tidiers
#' @aliases dbscan_tidiers glance tidy augment
#' @family tidiers
#'
#' @seealso [generics::tidy()], [generics::augment()],
#' [generics::glance()], [dbscan()]
#'
#' @examplesIf requireNamespace("tibble", quietly = TRUE) && identical(Sys.getenv("NOT_CRAN"), "true")
#'
#' data(iris)
#' x <- scale(iris[, 1:4])
#'
#' ## dbscan
#' db <- dbscan(x, eps = .9, minPts = 5)
#' db
#'
#' # summarize model fit with tidiers
#' tidy(db)
#' glance(db)
#'
#' # augment for this model needs the original data
#' augment(db, x)
#'
#' # to augment new data, the original data is also needed
#' augment(db, x, newdata = x[1:5, ])
#'
#' ## hdbscan
#' hdb <- hdbscan(x, minPts = 5)
#'
#' # summarize model fit with tidiers
#' tidy(hdb)
#' glance(hdb)
#'
#' # augment for this model needs the original data
#' augment(hdb, x)
#'
#' # to augment new data, the original data is also needed
#' augment(hdb, x, newdata = x[1:5, ])
#'
#' ## Jarvis-Patrick clustering
#' cl <- jpclust(x, k = 20, kt = 15)
#'
#' # summarize model fit with tidiers
#' tidy(cl)
#' glance(cl)
#'
#' # augment for this model needs the original data
#' augment(cl, x)
#'
#' ## Shared Nearest Neighbor clustering
#' cl <- sNNclust(x, k = 20, eps = 0.8, minPts = 15)
#'
#' # summarize model fit with tidiers
#' tidy(cl)
#' glance(cl)
#'
#' # augment for this model needs the original data
#' augment(cl, x)
#'
NULL
#' @rdname dbscan_tidiers
#' @importFrom generics tidy
#' @export
generics::tidy
#' @rdname dbscan_tidiers
#' @export
tidy.dbscan <- function(x, ...) {
n_cl <- max(x$cluster)
size <- table(factor(x$cluster, levels = 0:n_cl))
tb <- tibble::tibble(cluster = as.factor(0:n_cl),
size = as.integer(size))
tb$noise <- tb$cluster == 0L
tb
}
#' @rdname dbscan_tidiers
#' @export
tidy.hdbscan <- function(x, ...) {
n_cl <- max(x$cluster)
size <- table(factor(x$cluster, levels = 0:n_cl))
tb <- tibble::tibble(cluster = as.factor(0:n_cl),
size = as.integer(size))
tb$cluster_score <- as.numeric(x$cluster_scores[as.character(tb$cluster)])
tb$noise <- tb$cluster == 0L
tb
}
#' @rdname dbscan_tidiers
#' @export
tidy.general_clustering <- function(x, ...) {
n_cl <- max(x$cluster)
size <- table(factor(x$cluster, levels = 0:n_cl))
tb <- tibble::tibble(cluster = as.factor(0:n_cl),
size = as.integer(size))
tb$noise <- tb$cluster == 0L
tb
}
## augment
#' @importFrom generics augment
#' @rdname dbscan_tidiers
#' @export
generics::augment
#' @rdname dbscan_tidiers
#' @export
augment.dbscan <- function(x, data = NULL, newdata = NULL, ...) {
n_cl <- max(x$cluster)
if (is.null(data) && is.null(newdata))
stop("Must specify either `data` or `newdata` argument.")
if (is.null(data) || nrow(data) != length(x$cluster)) {
stop("The original data needs to be passed as data.")
}
if (is.null(newdata)) {
tb <- tibble::as_tibble(data)
tb$.cluster <- factor(x$cluster, levels = 0:n_cl)
} else {
tb <- tibble::as_tibble(newdata)
tb$.cluster <- factor(predict(x,
newdata = newdata,
data = data), levels = 0:n_cl)
}
tb$noise <- tb$.cluster == 0L
tb
}
#' @rdname dbscan_tidiers
#' @export
augment.hdbscan <- function(x, data = NULL, newdata = NULL, ...) {
n_cl <- max(x$cluster)
if (is.null(data) || nrow(data) != length(x$cluster)) {
stop("The original data needs to be passed as data.")
}
if (is.null(newdata)) {
tb <- tibble::as_tibble(data)
tb$.cluster <- factor(x$cluster, levels = 0:n_cl)
tb$.coredist <- x$coredist
tb$.membership_prob <- x$membership_prob
tb$.outlier_scores <- x$outlier_scores
} else {
tb <- tibble::as_tibble(newdata)
tb$.cluster <- factor(
predict(x, newdata = newdata, data = data), levels = 0:n_cl)
tb$.coredist <- NA_real_
tb$.membership_prob <- NA_real_
tb$.outlier_scores <- NA_real_
}
tb
}
#' @rdname dbscan_tidiers
#' @export
augment.general_clustering <- function(x, data = NULL, newdata = NULL, ...) {
n_cl <- max(x$cluster)
if (is.null(data) || nrow(data) != length(x$cluster)) {
stop("The original data needs to be passed as data.")
}
if (is.null(newdata)) {
tb <- tibble::as_tibble(data)
tb$.cluster <- factor(x$cluster, levels = 0:n_cl)
} else {
stop("augmenting new data is not supported.")
}
tb
}
## glance
#' @importFrom generics glance
#' @rdname dbscan_tidiers
#' @export
generics::glance
#' @rdname dbscan_tidiers
#' @export
glance.dbscan <- function(x, ...) {
tibble::tibble(
nobs = length(x$cluster),
n.clusters = length(table(x$cluster[x$cluster != 0L])),
nexcluded = sum(x$cluster == 0L)
)
}
#' @rdname dbscan_tidiers
#' @export
glance.hdbscan <- function(x, ...) {
tibble::tibble(
nobs = length(x$cluster),
n.clusters = length(table(x$cluster[x$cluster != 0L])),
nexcluded = sum(x$cluster == 0L)
)
}
#' @rdname dbscan_tidiers
#' @export
glance.general_clustering <- function(x, ...) {
tibble::tibble(
nobs = length(x$cluster),
n.clusters = length(table(x$cluster[x$cluster != 0L])),
nexcluded = sum(x$cluster == 0L)
)
}
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