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author | Joseph K. Bradley <joseph.kurata.bradley@gmail.com> | 2014-07-17 15:05:02 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-07-17 15:05:02 -0700 |
commit | 935fe65ff6559a0e3b481e7508fa14337b23020b (patch) | |
tree | ee298094fca9a7aead7c7e3f01abdd952ddc4845 | |
parent | 1fcd5dcdd8edb0e6989278c95e7f2c7d86c4efb2 (diff) | |
download | spark-935fe65ff6559a0e3b481e7508fa14337b23020b.tar.gz spark-935fe65ff6559a0e3b481e7508fa14337b23020b.tar.bz2 spark-935fe65ff6559a0e3b481e7508fa14337b23020b.zip |
SPARK-1215 [MLLIB]: Clustering: Index out of bounds error (2)
Added check to LocalKMeans.scala: kMeansPlusPlus initialization to handle case with fewer distinct data points than clusters k. Added two related unit tests to KMeansSuite. (Re-submitting PR after tangling commits in PR 1407 https://github.com/apache/spark/pull/1407 )
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes #1468 from jkbradley/kmeans-fix and squashes the following commits:
4e9bd1e [Joseph K. Bradley] Updated PR per comments from mengxr
6c7a2ec [Joseph K. Bradley] Added check to LocalKMeans.scala: kMeansPlusPlus initialization to handle case with fewer distinct data points than clusters k. Added two related unit tests to KMeansSuite.
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala | 8 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala | 26 |
2 files changed, 33 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala index 2e3a4ce783..f0722d7c14 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala @@ -59,7 +59,13 @@ private[mllib] object LocalKMeans extends Logging { cumulativeScore += weights(j) * KMeans.pointCost(curCenters, points(j)) j += 1 } - centers(i) = points(j-1).toDense + if (j == 0) { + logWarning("kMeansPlusPlus initialization ran out of distinct points for centers." + + s" Using duplicate point for center k = $i.") + centers(i) = points(0).toDense + } else { + centers(i) = points(j - 1).toDense + } } // Run up to maxIterations iterations of Lloyd's algorithm diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala index 560a4ad71a..76a3bdf9b1 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala @@ -61,6 +61,32 @@ class KMeansSuite extends FunSuite with LocalSparkContext { assert(model.clusterCenters.head === center) } + test("no distinct points") { + val data = sc.parallelize( + Array( + Vectors.dense(1.0, 2.0, 3.0), + Vectors.dense(1.0, 2.0, 3.0), + Vectors.dense(1.0, 2.0, 3.0)), + 2) + val center = Vectors.dense(1.0, 2.0, 3.0) + + // Make sure code runs. + var model = KMeans.train(data, k=2, maxIterations=1) + assert(model.clusterCenters.size === 2) + } + + test("more clusters than points") { + val data = sc.parallelize( + Array( + Vectors.dense(1.0, 2.0, 3.0), + Vectors.dense(1.0, 3.0, 4.0)), + 2) + + // Make sure code runs. + var model = KMeans.train(data, k=3, maxIterations=1) + assert(model.clusterCenters.size === 3) + } + test("single cluster with big dataset") { val smallData = Array( Vectors.dense(1.0, 2.0, 6.0), |