diff --git a/python/pyspark/ml/common.py b/python/pyspark/ml/common.py
index 256e91e14165e61da436b019bbf5c062d5b14c94..7d449aaccb44fde533edd45e9b60732e02751c66 100644
--- a/python/pyspark/ml/common.py
+++ b/python/pyspark/ml/common.py
@@ -63,7 +63,7 @@ def _to_java_object_rdd(rdd):
RDD is serialized in batch or not.
"""
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
- return rdd.ctx._jvm.MLSerDe.pythonToJava(rdd._jrdd, True)
+ return rdd.ctx._jvm.org.apache.spark.ml.python.MLSerDe.pythonToJava(rdd._jrdd, True)
def _py2java(sc, obj):
@@ -82,7 +82,7 @@ def _py2java(sc, obj):
pass
else:
data = bytearray(PickleSerializer().dumps(obj))
- obj = sc._jvm.MLSerDe.loads(data)
+ obj = sc._jvm.org.apache.spark.ml.python.MLSerDe.loads(data)
return obj
@@ -95,17 +95,17 @@ def _java2py(sc, r, encoding="bytes"):
clsName = 'JavaRDD'
if clsName == 'JavaRDD':
- jrdd = sc._jvm.MLSerDe.javaToPython(r)
+ jrdd = sc._jvm.org.apache.spark.ml.python.MLSerDe.javaToPython(r)
return RDD(jrdd, sc)
if clsName == 'Dataset':
return DataFrame(r, SQLContext.getOrCreate(sc))
if clsName in _picklable_classes:
- r = sc._jvm.MLSerDe.dumps(r)
+ r = sc._jvm.org.apache.spark.ml.python.MLSerDe.dumps(r)
elif isinstance(r, (JavaArray, JavaList)):
try:
- r = sc._jvm.MLSerDe.dumps(r)
+ r = sc._jvm.org.apache.spark.ml.python.MLSerDe.dumps(r)
except Py4JJavaError:
pass # not pickable
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 981ed9dda042c8d15215ce75926a00ae06e1a51c..24efce812b3b3759bda6291d6fd33844a2430c47 100755
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -1195,12 +1195,12 @@ class VectorTests(MLlibTestCase):
def _test_serialize(self, v):
self.assertEqual(v, ser.loads(ser.dumps(v)))
- jvec = self.sc._jvm.MLSerDe.loads(bytearray(ser.dumps(v)))
- nv = ser.loads(bytes(self.sc._jvm.MLSerDe.dumps(jvec)))
+ jvec = self.sc._jvm.org.apache.spark.ml.python.MLSerDe.loads(bytearray(ser.dumps(v)))
+ nv = ser.loads(bytes(self.sc._jvm.org.apache.spark.ml.python.MLSerDe.dumps(jvec)))
self.assertEqual(v, nv)
vs = [v] * 100
- jvecs = self.sc._jvm.MLSerDe.loads(bytearray(ser.dumps(vs)))
- nvs = ser.loads(bytes(self.sc._jvm.MLSerDe.dumps(jvecs)))
+ jvecs = self.sc._jvm.org.apache.spark.ml.python.MLSerDe.loads(bytearray(ser.dumps(vs)))
+ nvs = ser.loads(bytes(self.sc._jvm.org.apache.spark.ml.python.MLSerDe.dumps(jvecs)))
self.assertEqual(vs, nvs)
def test_serialize(self):
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index 95f7278dc64ce06e99d9e6070b139a89efa71845..93a0b64569b13dfb41dc86df6e03ce292f131d68 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -507,7 +507,7 @@ class GaussianMixtureModel(JavaModelWrapper, JavaSaveable, JavaLoader):
Path to where the model is stored.
"""
model = cls._load_java(sc, path)
- wrapper = sc._jvm.GaussianMixtureModelWrapper(model)
+ wrapper = sc._jvm.org.apache.spark.mllib.api.python.GaussianMixtureModelWrapper(model)
return cls(wrapper)
@@ -638,7 +638,8 @@ class PowerIterationClusteringModel(JavaModelWrapper, JavaSaveable, JavaLoader):
Load a model from the given path.
"""
model = cls._load_java(sc, path)
- wrapper = sc._jvm.PowerIterationClusteringModelWrapper(model)
+ wrapper =\
+ sc._jvm.org.apache.spark.mllib.api.python.PowerIterationClusteringModelWrapper(model)
return PowerIterationClusteringModel(wrapper)
diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py
index 31afdf576b677bd8ab47a85b36bda3254b6aa5cb..21f0e09ea7742dc4949e4ef376723187e748144b 100644
--- a/python/pyspark/mllib/common.py
+++ b/python/pyspark/mllib/common.py
@@ -66,7 +66,7 @@ def _to_java_object_rdd(rdd):
RDD is serialized in batch or not.
"""
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
- return rdd.ctx._jvm.SerDe.pythonToJava(rdd._jrdd, True)
+ return rdd.ctx._jvm.org.apache.spark.mllib.api.python.SerDe.pythonToJava(rdd._jrdd, True)
def _py2java(sc, obj):
@@ -85,7 +85,7 @@ def _py2java(sc, obj):
pass
else:
data = bytearray(PickleSerializer().dumps(obj))
- obj = sc._jvm.SerDe.loads(data)
+ obj = sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(data)
return obj
@@ -98,17 +98,17 @@ def _java2py(sc, r, encoding="bytes"):
clsName = 'JavaRDD'
if clsName == 'JavaRDD':
- jrdd = sc._jvm.SerDe.javaToPython(r)
+ jrdd = sc._jvm.org.apache.spark.mllib.api.python.SerDe.javaToPython(r)
return RDD(jrdd, sc)
if clsName == 'Dataset':
return DataFrame(r, SQLContext.getOrCreate(sc))
if clsName in _picklable_classes:
- r = sc._jvm.SerDe.dumps(r)
+ r = sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(r)
elif isinstance(r, (JavaArray, JavaList)):
try:
- r = sc._jvm.SerDe.dumps(r)
+ r = sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(r)
except Py4JJavaError:
pass # not pickable
diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index e31c75c1e8675dffd5214622f4ae374041e285c8..aef91a8ddc1f1e79a1a3dc7341b7ea46f2e4206a 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -553,7 +553,7 @@ class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader):
"""
jmodel = sc._jvm.org.apache.spark.mllib.feature \
.Word2VecModel.load(sc._jsc.sc(), path)
- model = sc._jvm.Word2VecModelWrapper(jmodel)
+ model = sc._jvm.org.apache.spark.mllib.api.python.Word2VecModelWrapper(jmodel)
return Word2VecModel(model)
diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py
index ab4066f7d68bae1a09341535f296050ba9fa3356..fb226e84e5d501615dc76dadd9c6bef77aed2a6c 100644
--- a/python/pyspark/mllib/fpm.py
+++ b/python/pyspark/mllib/fpm.py
@@ -64,7 +64,7 @@ class FPGrowthModel(JavaModelWrapper, JavaSaveable, JavaLoader):
Load a model from the given path.
"""
model = cls._load_java(sc, path)
- wrapper = sc._jvm.FPGrowthModelWrapper(model)
+ wrapper = sc._jvm.org.apache.spark.mllib.api.python.FPGrowthModelWrapper(model)
return FPGrowthModel(wrapper)
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 7e60255d43eade63b2b0624c0f7260e6422d58e3..732300ee9c2c9c757e5cd41e64dcb7627a8ceaf0 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -207,7 +207,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
def load(cls, sc, path):
"""Load a model from the given path"""
model = cls._load_java(sc, path)
- wrapper = sc._jvm.MatrixFactorizationModelWrapper(model)
+ wrapper = sc._jvm.org.apache.spark.mllib.api.python.MatrixFactorizationModelWrapper(model)
return MatrixFactorizationModel(wrapper)
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 72fa8b5f3d4772bda553f76c81529db8dbc121f6..99bf50b5a1640447326956d1ce960b2f30ef0d72 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -150,12 +150,12 @@ class VectorTests(MLlibTestCase):
def _test_serialize(self, v):
self.assertEqual(v, ser.loads(ser.dumps(v)))
- jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v)))
- nv = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvec)))
+ jvec = self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(bytearray(ser.dumps(v)))
+ nv = ser.loads(bytes(self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(jvec)))
self.assertEqual(v, nv)
vs = [v] * 100
- jvecs = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(vs)))
- nvs = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvecs)))
+ jvecs = self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(bytearray(ser.dumps(vs)))
+ nvs = ser.loads(bytes(self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(jvecs)))
self.assertEqual(vs, nvs)
def test_serialize(self):
@@ -1650,8 +1650,8 @@ class ALSTests(MLlibTestCase):
def test_als_ratings_serialize(self):
r = Rating(7, 1123, 3.14)
- jr = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(r)))
- nr = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jr)))
+ jr = self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(bytearray(ser.dumps(r)))
+ nr = ser.loads(bytes(self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(jr)))
self.assertEqual(r.user, nr.user)
self.assertEqual(r.product, nr.product)
self.assertAlmostEqual(r.rating, nr.rating, 2)
@@ -1659,7 +1659,8 @@ class ALSTests(MLlibTestCase):
def test_als_ratings_id_long_error(self):
r = Rating(1205640308657491975, 50233468418, 1.0)
# rating user id exceeds max int value, should fail when pickled
- self.assertRaises(Py4JJavaError, self.sc._jvm.SerDe.loads, bytearray(ser.dumps(r)))
+ self.assertRaises(Py4JJavaError, self.sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads,
+ bytearray(ser.dumps(r)))
class HashingTFTest(MLlibTestCase):