Add KMeans.setPredictionCol

feynmanliang · Jul 1, 2015 · 85d92b1 · 85d92b1
1 parent aa9469d
commit 85d92b1
Show file tree

Hide file tree

Showing 2 changed files with 11 additions and 4 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -150,6 +150,9 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
   /** @group setParam */
   def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
   /** @group setParam */
   def setK(value: Int): this.type = set(k, value)
 
@@ -187,7 +190,8 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
         .setMaxIterations(map(maxIter))
         .setSeed(map(seed))
     val parentModel = algo.run(rdd)
-    new KMeansModel(uid, map, parentModel)
+    val model = new KMeansModel(uid, map, parentModel)
+    copyValues(model)
   }
 
   override def transformSchema(schema: StructType): StructType = {

diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
@@ -50,6 +50,7 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     assert(kmeans.getK === 2)
     assert(kmeans.getFeaturesCol === "features")
+    assert(kmeans.getPredictionCol === "prediction")
     assert(kmeans.getMaxIter === 20)
     assert(kmeans.getRuns === 1)
     assert(kmeans.getInitializationMode === KMeans.K_MEANS_PARALLEL)
@@ -58,14 +59,16 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("fit & transform") {
-    val kmeans = new KMeans().setK(k)
+    val predictionColName = "kmeans_prediction"
+    val kmeans = new KMeans().setK(k).setPredictionCol(predictionColName)
     val model = kmeans.fit(dataset)
     assert(model.clusterCenters.length === k)
 
     val transformed = model.transform(dataset)
-    assert(transformed.columns === Array("features", "prediction"))
-    val clusters = transformed.select("prediction")
+    assert(transformed.columns === Array("features", predictionColName))
+    val clusters = transformed.select(predictionColName)
         .map(row => row.apply(0)).distinct().collect().toSet
+    assert(clusters.size == 5)
     assert(clusters === Set(0, 1, 2, 3, 4))
   }
 }