Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6737#discussion_r32782633
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala ---
@@ -278,6 +278,34 @@ class KMeansSuite extends SparkFunSuite with
MLlibTestSparkContext {
}
}
}
+
+ test("Initialize using given cluster centers") {
+ val points = Seq(
+ Vectors.dense(0.0, 0.0),
+ Vectors.dense(0.0, 0.1),
+ Vectors.dense(0.1, 0.0),
+ Vectors.dense(9.0, 0.0),
+ Vectors.dense(9.0, 0.2),
+ Vectors.dense(9.2, 0.0)
+ )
+ val rdd = sc.parallelize(points, 3)
+ val model = KMeans.train(rdd, k = 2, maxIterations = 2, runs = 1)
+
+ val tempDir = Utils.createTempDir()
+ val path = tempDir.toURI.toString
+ model.save(sc, path)
+ val loadedModel = KMeansModel.load(sc, path)
+
+ val newModel = KMeans.train(rdd, k = 2, maxIterations = 2,
initialModel = loadedModel)
--- End diff --
This seems like a somewhat brittle test. It requires that the initial
training find the correct centers. I imagine it generally would, but with so
few data, I suspect it will fail every now and then.
A better test might be:
* have 4 data points A,B,C,D at the corners of a square
* use k = 2
* compare the results from starting with initial centers at A,C vs. B,D,
using maxIterations = 1
(Basically, cluster centers should not move, so they should definitely be
different in the end.)
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