[GitHub] felixcheung commented on a change in pull request #23072: [SPARK-19827][R]spark.ml R API for PIC
felixcheung commented on a change in pull request #23072:
[SPARK-19827][R]spark.ml R API for PIC
URL: https://github.com/apache/spark/pull/23072#discussion_r240491948
##
File path: R/pkg/R/mllib_clustering.R
##
@@ -610,3 +616,59 @@ 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.
+#'
Review comment:
remove empty line - empty is significant in roxygen2
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[GitHub] felixcheung commented on a change in pull request #23072: [SPARK-19827][R]spark.ml R API for PIC
felixcheung commented on a change in pull request #23072:
[SPARK-19827][R]spark.ml R API for PIC
URL: https://github.com/apache/spark/pull/23072#discussion_r240492041
##
File path: R/pkg/R/mllib_clustering.R
##
@@ -610,3 +616,59 @@ 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 the initialization algorithm.
Review comment:
add `One of "random", "degree"`?
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[GitHub] felixcheung commented on a change in pull request #23072: [SPARK-19827][R]spark.ml R API for PIC
felixcheung commented on a change in pull request #23072:
[SPARK-19827][R]spark.ml R API for PIC
URL: https://github.com/apache/spark/pull/23072#discussion_r240492482
##
File path: R/pkg/R/mllib_clustering.R
##
@@ -610,3 +616,59 @@ 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 the initialization algorithm.
+#' @param maxIter the maximum number of iterations.
+#' @param sourceCol the name of the input column for source vertex IDs.
+#' @param destinationCol the name of the input column for destination vertex
IDs
+#' @param weightCol 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}
Review comment:
mm, this won't format correctly - roxygen strips all the whitespaces
also Long and Int is not a proper type in R
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[GitHub] felixcheung commented on a change in pull request #23072: [SPARK-19827][R]spark.ml R API for PIC
felixcheung commented on a change in pull request #23072:
[SPARK-19827][R]spark.ml R API for PIC
URL: https://github.com/apache/spark/pull/23072#discussion_r240492887
##
File path: R/pkg/R/mllib_clustering.R
##
@@ -610,3 +616,59 @@ 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 the initialization algorithm.
+#' @param maxIter the maximum number of iterations.
+#' @param sourceCol the name of the input column for source vertex IDs.
+#' @param destinationCol the name of the input column for destination vertex
IDs
+#' @param weightCol 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")
Review comment:
space around `=` as style
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[GitHub] felixcheung commented on a change in pull request #23072: [SPARK-19827][R]spark.ml R API for PIC
felixcheung commented on a change in pull request #23072:
[SPARK-19827][R]spark.ml R API for PIC
URL: https://github.com/apache/spark/pull/23072#discussion_r240492789
##
File path: R/pkg/R/mllib_clustering.R
##
@@ -610,3 +616,59 @@ 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 the initialization algorithm.
+#' @param maxIter the maximum number of iterations.
+#' @param sourceCol the name of the input column for source vertex IDs.
+#' @param destinationCol the name of the input column for destination vertex
IDs
+#' @param weightCol 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
Review comment:
wait, this aliases doesn't make sense. could you test if `?assignClusters`
in R shell if this works?
this should be `@aliases spark.assignClusters,SparkDataFrame-method`
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[GitHub] felixcheung commented on a change in pull request #23072: [SPARK-19827][R]spark.ml R API for PIC
felixcheung commented on a change in pull request #23072:
[SPARK-19827][R]spark.ml R API for PIC
URL: https://github.com/apache/spark/pull/23072#discussion_r240493499
##
File path: R/pkg/R/mllib_clustering.R
##
@@ -610,3 +616,59 @@ 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 the initialization algorithm.
+#' @param maxIter the maximum number of iterations.
+#' @param sourceCol the name of the input column for source vertex IDs.
+#' @param destinationCol the name of the input column for destination vertex
IDs
+#' @param weightCol 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) {
Review comment:
if maxIter should in integer, should we check k is also integer? it;s fixed
when it is passed, so just a minor consistency on value check
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