[SPARK-17389][Follow-up][ML] Change KMeans k-means|| default init steps from 5 to 2.

mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala

@@ -69,7 +69,7 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
 
   /**
    * Param for the number of steps for the k-means|| initialization mode. This is an advanced
-   * setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.
+   * setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.
    * @group expertParam
    */
   @Since("1.5.0")
@@ -262,7 +262,7 @@ class KMeans @Since("1.5.0") (
     k -> 2,
     maxIter -> 20,
     initMode -> MLlibKMeans.K_MEANS_PARALLEL,
-    initSteps -> 5,
+    initSteps -> 2,
     tol -> 1e-4)
 
   @Since("1.5.0")

mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala

@@ -45,7 +45,7 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR
     assert(kmeans.getPredictionCol === "prediction")
     assert(kmeans.getMaxIter === 20)
     assert(kmeans.getInitMode === MLlibKMeans.K_MEANS_PARALLEL)
-    assert(kmeans.getInitSteps === 5)
+    assert(kmeans.getInitSteps === 2)
     assert(kmeans.getTol === 1e-4)
   }
 

python/pyspark/ml/clustering.py

@@ -254,14 +254,14 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol
 
     @keyword_only
     def __init__(self, featuresCol="features", predictionCol="prediction", k=2,
-                 initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
+                 initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20, seed=None):
         """
         __init__(self, featuresCol="features", predictionCol="prediction", k=2, \
-                 initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
+                 initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20, seed=None)
         """
         super(KMeans, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
-        self._setDefault(k=2, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20)
+        self._setDefault(k=2, initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -271,10 +271,10 @@ def _create_model(self, java_model):
     @keyword_only
     @since("1.5.0")
     def setParams(self, featuresCol="features", predictionCol="prediction", k=2,
-                  initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
+                  initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20, seed=None):
         """
         setParams(self, featuresCol="features", predictionCol="prediction", k=2, \
-                  initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
+                  initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20, seed=None)
 
         Sets params for KMeans.
         """

python/pyspark/mllib/clustering.py

@@ -306,7 +306,7 @@ class KMeans(object):
     @classmethod
     @since('0.9.0')
     def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
-              seed=None, initializationSteps=5, epsilon=1e-4, initialModel=None):
+              seed=None, initializationSteps=2, epsilon=1e-4, initialModel=None):
         """
         Train a k-means clustering model.
 
@@ -330,9 +330,9 @@ def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||"
           (default: None)
         :param initializationSteps:
           Number of steps for the k-means|| initialization mode.
-          This is an advanced setting -- the default of 5 is almost
+          This is an advanced setting -- the default of 2 is almost
           always enough.
-          (default: 5)
+          (default: 2)
         :param epsilon:
           Distance threshold within which a center will be considered to
           have converged. If all centers move less than this Euclidean