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[SPARK-19319][SparkR]:SparkR Kmeans summary returns error when the cluster size doesn't equal to k #16666

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[SPARK-19319][SparkR]:SparkR Kmeans summary returns error when the cluster size doesn't equal to k #16666

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2c1d02d
fix kmeans bug
wangmiao1981 Jan 21, 2017
d1a2d6c
add is.loaded in comment
wangmiao1981 Jan 23, 2017
52c9eb1
Merge github.com:apache/spark into kmeans
wangmiao1981 Jan 31, 2017
72fb951
add fix
wangmiao1981 Jan 31, 2017
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15 changes: 9 additions & 6 deletions R/pkg/R/mllib_clustering.R
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Expand Up @@ -225,10 +225,12 @@ 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), and \code{clusterSize}
#' (the actual number of cluster centers. When using initMode = "random",
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let's add is.loaded here

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OK. I will add it. For bisecting kmeans, I haven't found a case like this. This case only occurs when initMode is random and this behavior was due to one fix to kmeans implementation.

#' \code{clusterSize} may not equal to \code{k}).
#' @rdname spark.kmeans
#' @export
#' @note summary(KMeansModel) since 2.0.0
Expand All @@ -240,16 +242,17 @@ setMethod("summary", signature(object = "KMeansModel"),
coefficients <- callJMethod(jobj, "coefficients")
k <- callJMethod(jobj, "k")
size <- callJMethod(jobj, "size")
coefficients <- t(matrix(coefficients, ncol = k))
clusterSize <- callJMethod(jobj, "clusterSize")
coefficients <- t(matrix(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
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15 changes: 11 additions & 4 deletions R/pkg/inst/tests/testthat/test_mllib_clustering.R
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Expand Up @@ -145,13 +145,20 @@ 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", {
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2 changes: 2 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/r/KMeansWrapper.scala
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Expand Up @@ -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)
Expand Down