[SPARK-9828] [PySpark] Mutable values should not be default arguments

python/pyspark/ml/evaluation.py

@@ -45,7 +45,7 @@ def _evaluate(self, dataset):
         """
         raise NotImplementedError()
 
-    def evaluate(self, dataset, params={}):
+    def evaluate(self, dataset, params=None):
         """
         Evaluates the output with optional parameters.
 
@@ -55,6 +55,8 @@ def evaluate(self, dataset, params={}):
                        params
         :return: metric
         """
+        if params is None:
+            params = dict()
         if isinstance(params, dict):
             if params:
                 return self.copy(params)._evaluate(dataset)

python/pyspark/ml/param/__init__.py

@@ -60,14 +60,16 @@ class Params(Identifiable):
 
     __metaclass__ = ABCMeta
 
-    #: internal param map for user-supplied values param map
-    _paramMap = {}
+    def __init__(self):
+        super(Params, self).__init__()
+        #: internal param map for user-supplied values param map
+        self._paramMap = {}
 
-    #: internal param map for default values
-    _defaultParamMap = {}
+        #: internal param map for default values
+        self._defaultParamMap = {}
 
-    #: value returned by :py:func:`params`
-    _params = None
+        #: value returned by :py:func:`params`
+        self._params = None
 
     @property
     def params(self):
@@ -155,7 +157,7 @@ def getOrDefault(self, param):
         else:
             return self._defaultParamMap[param]
 
-    def extractParamMap(self, extra={}):
+    def extractParamMap(self, extra=None):
         """
         Extracts the embedded default param values and user-supplied
         values, and then merges them with extra values from input into
@@ -165,12 +167,14 @@ def extractParamMap(self, extra={}):
         :param extra: extra param values
         :return: merged param map
         """
+        if extra is None:
+            extra = dict()
         paramMap = self._defaultParamMap.copy()
         paramMap.update(self._paramMap)
         paramMap.update(extra)
         return paramMap
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with the same uid and some
         extra params. The default implementation creates a
@@ -181,6 +185,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         that = copy.copy(self)
         that._paramMap = self.extractParamMap(extra)
         return that
@@ -233,14 +239,16 @@ def _setDefault(self, **kwargs):
             self._defaultParamMap[getattr(self, param)] = value
         return self
 
-    def _copyValues(self, to, extra={}):
+    def _copyValues(self, to, extra=None):
         """
         Copies param values from this instance to another instance for
         params shared by them.
         :param to: the target instance
         :param extra: extra params to be copied
         :return: the target instance with param values copied
         """
+        if extra is None:
+            extra = dict()
         paramMap = self.extractParamMap(extra)
         for p in self.params:
             if p in paramMap and to.hasParam(p.name):

python/pyspark/ml/pipeline.py

@@ -141,7 +141,7 @@ class Pipeline(Estimator):
     @keyword_only
     def __init__(self, stages=None):
         """
-        __init__(self, stages=[])
+        __init__(self, stages=None)
         """
         if stages is None:
             stages = []
@@ -170,7 +170,7 @@ def getStages(self):
     @keyword_only
     def setParams(self, stages=None):
         """
-        setParams(self, stages=[])
+        setParams(self, stages=None)
         Sets params for Pipeline.
         """
         if stages is None:

python/pyspark/ml/tuning.py

@@ -227,7 +227,9 @@ def _fit(self, dataset):
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
+        if extra is None:
+            extra = dict()
         newCV = Params.copy(self, extra)
         if self.isSet(self.estimator):
             newCV.setEstimator(self.getEstimator().copy(extra))
@@ -250,7 +252,7 @@ def __init__(self, bestModel):
     def _transform(self, dataset):
         return self.bestModel.transform(dataset)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with a randomly generated uid
         and some extra params. This copies the underlying bestModel,
@@ -259,6 +261,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         return CrossValidatorModel(self.bestModel.copy(extra))
 
 

python/pyspark/rdd.py

@@ -700,7 +700,7 @@ def groupBy(self, f, numPartitions=None):
         return self.map(lambda x: (f(x), x)).groupByKey(numPartitions)
 
     @ignore_unicode_prefix
-    def pipe(self, command, env={}, checkCode=False):
+    def pipe(self, command, env=None, checkCode=False):
         """
         Return an RDD created by piping elements to a forked external process.
 
@@ -709,6 +709,9 @@ def pipe(self, command, env={}, checkCode=False):
 
         :param checkCode: whether or not to check the return value of the shell command.
         """
+        if env is None:
+            env = dict()
+
         def func(iterator):
             pipe = Popen(
                 shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)

python/pyspark/sql/readwriter.py

@@ -182,7 +182,7 @@ def orc(self, path):
 
     @since(1.4)
     def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
-             predicates=None, properties={}):
+             predicates=None, properties=None):
         """
         Construct a :class:`DataFrame` representing the database table accessible
         via JDBC URL `url` named `table` and connection `properties`.
@@ -208,6 +208,8 @@ def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPar
                            should be included.
         :return: a DataFrame
         """
+        if properties is None:
+            properties = dict()
         jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
         for k in properties:
             jprop.setProperty(k, properties[k])
@@ -427,7 +429,7 @@ def orc(self, path, mode=None, partitionBy=None):
         self._jwrite.orc(path)
 
     @since(1.4)
-    def jdbc(self, url, table, mode=None, properties={}):
+    def jdbc(self, url, table, mode=None, properties=None):
         """Saves the content of the :class:`DataFrame` to a external database table via JDBC.
 
         .. note:: Don't create too many partitions in parallel on a large cluster;\
@@ -445,6 +447,8 @@ def jdbc(self, url, table, mode=None, properties={}):
                            arbitrary string tag/value. Normally at least a
                            "user" and "password" property should be included.
         """
+        if properties is None:
+            properties = dict()
         jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
         for k in properties:
             jprop.setProperty(k, properties[k])

python/pyspark/statcounter.py

@@ -30,7 +30,9 @@
 
 class StatCounter(object):
 
-    def __init__(self, values=[]):
+    def __init__(self, values=None):
+        if values is None:
+            values = list()
         self.n = 0    # Running count of our values
         self.mu = 0.0  # Running mean of our values
         self.m2 = 0.0  # Running variance numerator (sum of (x - mean)^2)

python/pyspark/streaming/kafka.py

@@ -35,7 +35,7 @@ def utf8_decoder(s):
 class KafkaUtils(object):
 
     @staticmethod
-    def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
+    def createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
                      storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
                      keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
@@ -52,6 +52,8 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder)
         :return: A DStream object
         """
+        if kafkaParams is None:
+            kafkaParams = dict()
         kafkaParams.update({
             "zookeeper.connect": zkQuorum,
             "group.id": groupId,
@@ -77,7 +79,7 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
         return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
 
     @staticmethod
-    def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
+    def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None,
                            keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         .. note:: Experimental
@@ -105,6 +107,8 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder).
         :return: A DStream object
         """
+        if fromOffsets is None:
+            fromOffsets = dict()
         if not isinstance(topics, list):
             raise TypeError("topics should be list")
         if not isinstance(kafkaParams, dict):
@@ -129,7 +133,7 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
         return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)
 
     @staticmethod
-    def createRDD(sc, kafkaParams, offsetRanges, leaders={},
+    def createRDD(sc, kafkaParams, offsetRanges, leaders=None,
                   keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         .. note:: Experimental
@@ -145,6 +149,8 @@ def createRDD(sc, kafkaParams, offsetRanges, leaders={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder)
         :return: A RDD object
         """
+        if leaders is None:
+            leaders = dict()
         if not isinstance(kafkaParams, dict):
             raise TypeError("kafkaParams should be dict")
         if not isinstance(offsetRanges, list):