[SPARK-19319][SparkR]:SparkR Kmeans summary returns error when the cluster size doesn't equal to k

R/pkg/R/mllib_clustering.R

@@ -375,10 +375,13 @@ setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula"
 
 #' @param object a fitted k-means model.
 #' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes the model's \code{k} (number of cluster centers),
+#'         The list includes the model's \code{k} (the configured number of cluster centers),
 #'         \code{coefficients} (model cluster centers),
-#'         \code{size} (number of data points in each cluster), and \code{cluster}
-#'         (cluster centers of the transformed data).
+#'         \code{size} (number of data points in each cluster), \code{cluster}
+#'         (cluster centers of the transformed data), {is.loaded} (whether the model is loaded
+#'         from a saved file), and \code{clusterSize}
+#'         (the actual number of cluster centers. When using initMode = "random",
+#'         \code{clusterSize} may not equal to \code{k}).
 #' @rdname spark.kmeans
 #' @export
 #' @note summary(KMeansModel) since 2.0.0
@@ -390,16 +393,17 @@ setMethod("summary", signature(object = "KMeansModel"),
             coefficients <- callJMethod(jobj, "coefficients")
             k <- callJMethod(jobj, "k")
             size <- callJMethod(jobj, "size")
-            coefficients <- t(matrix(unlist(coefficients), ncol = k))
+            clusterSize <- callJMethod(jobj, "clusterSize")
+            coefficients <- t(matrix(unlist(coefficients), ncol = clusterSize))
             colnames(coefficients) <- unlist(features)
-            rownames(coefficients) <- 1:k
+            rownames(coefficients) <- 1:clusterSize
             cluster <- if (is.loaded) {
               NULL
             } else {
               dataFrame(callJMethod(jobj, "cluster"))
             }
             list(k = k, coefficients = coefficients, size = size,
-                 cluster = cluster, is.loaded = is.loaded)
+                 cluster = cluster, is.loaded = is.loaded, clusterSize = clusterSize)
           })
 
 #  Predicted values based on a k-means model

R/pkg/inst/tests/testthat/test_mllib_clustering.R

@@ -196,13 +196,20 @@ test_that("spark.kmeans", {
   model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10,
                          initMode = "random", seed = 22222, tol = 1E-5)
 
-  fitted.model1 <- fitted(model1)
-  fitted.model2 <- fitted(model2)
+  summary.model1 <- summary(model1)
+  summary.model2 <- summary(model2)
+  cluster1 <- summary.model1$cluster
+  cluster2 <- summary.model2$cluster
+  clusterSize1 <- summary.model1$clusterSize
+  clusterSize2 <- summary.model2$clusterSize
+
   # The predicted clusters are different
-  expect_equal(sort(collect(distinct(select(fitted.model1, "prediction")))$prediction),
+  expect_equal(sort(collect(distinct(select(cluster1, "prediction")))$prediction),
              c(0, 1, 2, 3))
-  expect_equal(sort(collect(distinct(select(fitted.model2, "prediction")))$prediction),
+  expect_equal(sort(collect(distinct(select(cluster2, "prediction")))$prediction),
              c(0, 1, 2))
+  expect_equal(clusterSize1, 4)
+  expect_equal(clusterSize2, 3)
 })
 
 test_that("spark.lda with libsvm", {

mllib/src/main/scala/org/apache/spark/ml/r/KMeansWrapper.scala

@@ -43,6 +43,8 @@ private[r] class KMeansWrapper private (
 
   lazy val cluster: DataFrame = kMeansModel.summary.cluster
 
+  lazy val clusterSize: Int = kMeansModel.clusterCenters.size
+
   def fitted(method: String): DataFrame = {
     if (method == "centers") {
       kMeansModel.summary.predictions.drop(kMeansModel.getFeaturesCol)