Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/11119#discussion_r55967102
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala ---
@@ -108,6 +113,21 @@ class KMeansSuite extends SparkFunSuite with
MLlibTestSparkContext with DefaultR
val kmeans = new KMeans()
testEstimatorAndModelReadWrite(kmeans, dataset,
KMeansSuite.allParamSettings, checkModelData)
}
+
+ test("Initialize using given cluster centers") {
+ val kmeans = new KMeans()
+ .setK(k)
+ .setSeed(1)
+ .setInitialModel(initialModel)
+ val model = kmeans.fit(dataset)
+
+ // Converged initial model should lead to only a single iteration.
+ val convergedModel =
kmeans.setInitialModel(model).fit(dataset).clusterCenters
+ val oneIterationModel =
kmeans.setInitialModel(model).setMaxIter(1).fit(dataset).clusterCenters
--- End diff --
Maybe what we should check it that with `setMaxIter` high values, the
algorithm only use one iteration to converge.
Also, nicer to have another test which is
```
val almostConvergedModel =
kmeans.setInitialModel(initialModel).setMaxIter(5).fit(dataset).clusterCenters
```
And then use it to train another k-means which should significantly reduce
the # of iteration without warn start.
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