Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/23072#discussion_r239259444
--- Diff: R/pkg/R/mllib_clustering.R ---
@@ -610,3 +616,58 @@ setMethod("write.ml", signature(object = "LDAModel",
path = "character"),
function(object, path, overwrite = FALSE) {
write_internal(object, path, overwrite)
})
+
+#' PowerIterationClustering
+#'
+#' A scalable graph clustering algorithm. Users can call
\code{spark.assignClusters} to
+#' return a cluster assignment for each input vertex.
+#'
+# Run the PIC algorithm and returns a cluster assignment for each input
vertex.
+#' @param data A SparkDataFrame.
+#' @param k The number of clusters to create.
+#' @param initMode Param for the initialization algorithm.
+#' @param maxIter Param for maximum number of iterations.
+#' @param sourceCol Param for the name of the input column for source
vertex IDs.
+#' @param destinationCol Name of the input column for destination vertex
IDs.
+#' @param weightCol Param for weight column name. If this is not set or
\code{NULL},
+#' we treat all instance weights as 1.0.
+#' @param ... additional argument(s) passed to the method.
+#' @return A dataset that contains columns of vertex id and the
corresponding cluster for the id.
+#' The schema of it will be:
+#' \code{id: Long}
+#' \code{cluster: Int}
+#' @rdname spark.powerIterationClustering
+#' @aliases
assignClusters,PowerIterationClustering-method,SparkDataFrame-method
+#' @examples
+#' \dontrun{
+#' df <- createDataFrame(list(list(0L, 1L, 1.0), list(0L, 2L, 1.0),
+#' list(1L, 2L, 1.0), list(3L, 4L, 1.0),
+#' list(4L, 0L, 0.1)), schema = c("src", "dst",
"weight"))
+#' clusters <- spark.assignClusters(df, initMode="degree",
weightCol="weight")
+#' showDF(clusters)
+#' }
+#' @note spark.assignClusters(SparkDataFrame) since 3.0.0
+setMethod("spark.assignClusters",
+ signature(data = "SparkDataFrame"),
+ function(data, k = 2L, initMode = c("random", "degree"), maxIter
= 20L,
+ sourceCol = "src", destinationCol = "dst", weightCol = NULL) {
+ if (!is.numeric(k) || k < 1) {
+ stop("k should be a number with value >= 1.")
+ }
+ if (!is.integer(maxIter) || maxIter <= 0) {
+ stop("maxIter should be a number with value > 0.")
+ }
--- End diff --
I mean the `data` SparkDataFrame's column types, if possible. If you remove
'L' from '0L' in your example dataset, you can see the failure.
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