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

## What changes were proposed in this pull request?
#14956 reduced default k-means|| init steps to 2 from 5 only for spark.mllib package, we should also do same change for spark.ml and PySpark.

## How was this patch tested?
Existing tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15050 from yanboliang/spark-17389.

2 files changed, 8 insertions, 8 deletions

diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index 4dab83362a..7632f05c3b 100644
--- a/python/pyspark/ml/clustering.py
+++ b/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 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol
     @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.
         """
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index 29aa615125..2036168e45 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/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 @@ class KMeans(object):
           (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