diff options
Diffstat (limited to 'python/pyspark')
| -rw-r--r-- | python/pyspark/ml/clustering.py | 5 | ||||
| -rw-r--r-- | python/pyspark/mllib/clustering.py | 9 |
2 files changed, 5 insertions, 9 deletions
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index 4ce8012754..9740ec45af 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -194,9 +194,8 @@ class KMeansModel(JavaModel, JavaMLWritable, JavaMLReadable): class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed, JavaMLWritable, JavaMLReadable): """ - K-means clustering with support for multiple parallel runs and a k-means++ like initialization - mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested, - they are executed together with joint passes over the data for efficiency. + K-means clustering with a k-means++ like initialization mode + (the k-means|| algorithm by Bahmani et al). >>> from pyspark.mllib.linalg import Vectors >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),), diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index 23d118bd40..95f7278dc6 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -179,7 +179,7 @@ class KMeansModel(Saveable, Loader): >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2) >>> model = KMeans.train( - ... sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random", + ... sc.parallelize(data), 2, maxIterations=10, initializationMode="random", ... seed=50, initializationSteps=5, epsilon=1e-4) >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0])) True @@ -323,9 +323,7 @@ class KMeans(object): Maximum number of iterations allowed. (default: 100) :param runs: - Number of runs to execute in parallel. The best model according - to the cost function will be returned (deprecated in 1.6.0). - (default: 1) + This param has no effect since Spark 2.0.0. :param initializationMode: The initialization algorithm. This can be either "random" or "k-means||". @@ -350,8 +348,7 @@ class KMeans(object): (default: None) """ if runs != 1: - warnings.warn( - "Support for runs is deprecated in 1.6.0. This param will have no effect in 2.0.0.") + warnings.warn("The param `runs` has no effect since Spark 2.0.0.") clusterInitialModel = [] if initialModel is not None: if not isinstance(initialModel, KMeansModel): |