Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/12813#discussion_r61669606
--- Diff: R/pkg/R/mllib.R ---
@@ -271,22 +274,25 @@ setMethod("summary", signature(object =
"NaiveBayesModel"),
#' Fit a k-means model, similarly to R's kmeans().
#'
#' @param data SparkDataFrame for training
-#' @param k Number of centers
-#' @param maxIter Maximum iteration number
-#' @param initializationMode Algorithm choosen to fit the model
+#' @param formula A symbolic description of the model to be fitted.
Currently only a few formula
+#' operators are supported, including '~', '.', ':', '+',
and '-'.
+#' Note that the response variable of formula is empty in
spark.kmeans.
+#' @param centers Number of centers
+#' @param iter.max Maximum iteration number
+#' @param algorithm The initialization algorithm choosen to fit the model
#' @return A fitted k-means model
#' @rdname spark.kmeans
#' @export
#' @examples
#' \dontrun{
-#' model <- spark.kmeans(data, k = 2, initializationMode="random")
+#' model <- spark.kmeans(data, ~ ., centers = 2, algorithm="random")
#' }
-setMethod("spark.kmeans", signature(data = "SparkDataFrame"),
- function(data, k, maxIter = 10, initializationMode = c("random",
"k-means||")) {
- columnNames <- as.array(colnames(data))
- initializationMode <- match.arg(initializationMode)
- jobj <- callJStatic("org.apache.spark.ml.r.KMeansWrapper",
"fit", data@sdf,
- k, maxIter, initializationMode,
columnNames)
+setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula =
"formula"),
+ function(data, formula, centers, iter.max = 10, algorithm =
c("random", "k-means||")) {
--- End diff --
Since `spark.kmeans` already indicates that this method is different from
R's `kmeans`, we made the param names consistent with MLlib params. Especially
the change from `algorithm` to `initMode` makes more sense. We should discuss
whether we want to use `maxIter`/`initMode` or `max.iter`/`init.mode` as params.
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