Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17117#discussion_r104800325
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala ---
@@ -152,6 +158,35 @@ class KMeansSuite extends SparkFunSuite with
MLlibTestSparkContext with DefaultR
val kmeans = new KMeans()
testEstimatorAndModelReadWrite(kmeans, dataset,
KMeansSuite.allParamSettings, checkModelData)
}
+
+ test("training with initial model") {
+ val kmeans = new KMeans().setK(2).setSeed(1)
+ val model1 = kmeans.fit(rData)
+ val model2 =
kmeans.setInitMode("initialModel").setInitialModel(model1).fit(rData)
+ model2.clusterCenters.zip(model1.clusterCenters)
+ .foreach { case (center2, center1) => assert(center2 ~== center1
absTol 1E-8) }
+ }
+
+ test("training with initial model, error cases") {
+ val kmeans = new KMeans().setK(k).setSeed(1).setMaxIter(1)
+
+ // Sets initMode with 'initialModel', but does not specify initial
model.
+ intercept[IllegalArgumentException] {
--- End diff --
My 2cents is the latter configuration should be able to overwrite the
former settings and related settings with warning messages.
In your example, when `kmeans.setInitMode("k-means||")` is performed, the
first `setInitialModel` should be ignored with warning message.
Even we do `setK(k =3)`, and later we do `.setInitialModel(initialModel)`,
we should ignore the first `setK(k =3)` with warning.
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