From 9ac05225e870e41dc86cd6d61c7f0d111d172810 Mon Sep 17 00:00:00 2001 From: "wm624@hotmail.com" Date: Tue, 31 Jan 2017 21:16:37 -0800 Subject: [SPARK-19319][SPARKR] SparkR Kmeans summary returns error when the cluster size doesn't equal to k ## What changes were proposed in this pull request When Kmeans using initMode = "random" and some random seed, it is possible the actual cluster size doesn't equal to the configured `k`. In this case, summary(model) returns error due to the number of cols of coefficient matrix doesn't equal to k. Example: > col1 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0) > col2 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0) > col3 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0) > cols <- as.data.frame(cbind(col1, col2, col3)) > df <- createDataFrame(cols) > > model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10, initMode = "random", seed = 22222, tol = 1E-5) > > summary(model2) Error in `colnames<-`(`*tmp*`, value = c("col1", "col2", "col3")) : length of 'dimnames' [2] not equal to array extent In addition: Warning message: In matrix(coefficients, ncol = k) : data length [9] is not a sub-multiple or multiple of the number of rows [2] Fix: Get the actual cluster size in the summary and use it to build the coefficient matrix. ## How was this patch tested? Add unit tests. Author: wm624@hotmail.com Closes #16666 from wangmiao1981/kmeans. --- R/pkg/inst/tests/testthat/test_mllib_clustering.R | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) (limited to 'R/pkg/inst/tests') diff --git a/R/pkg/inst/tests/testthat/test_mllib_clustering.R b/R/pkg/inst/tests/testthat/test_mllib_clustering.R index 28a6eeba2c..1661e987b7 100644 --- a/R/pkg/inst/tests/testthat/test_mllib_clustering.R +++ b/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", { -- cgit v1.2.3