diff options
4 files changed, 11 insertions, 11 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 index 6c46be7196..b04e82838e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala +++ b/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") 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 index 88f31a1cd2..c9ba5a288a 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala +++ b/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) } 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 |