def corr(x, y=None, method=None):
"""
Compute the correlation (matrix) for the input RDD(s) using the
specified method.
Methods currently supported: I{pearson (default), spearman}.
If a single RDD of Vectors is passed in, a correlation matrix
comparing the columns in the input RDD is returned. Use C{method=}
to specify the method to be used for single RDD inout.
If two RDDs of floats are passed in, a single float is returned.
>>> x = sc.parallelize([1.0, 0.0, -2.0], 2)
>>> y = sc.parallelize([4.0, 5.0, 3.0], 2)
>>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2)
>>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7
True
>>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson")
True
>>> Statistics.corr(x, y, "spearman")
0.5
>>> from math import isnan
>>> isnan(Statistics.corr(x, zeros))
True
>>> from pyspark.mllib.linalg import Vectors
>>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])])
>>> pearsonCorr = Statistics.corr(rdd)
>>> print str(pearsonCorr).replace('nan', 'NaN')
[[ 1. 0.05564149 NaN 0.40047142]
[ 0.05564149 1. NaN 0.91359586]
[ NaN NaN 1. NaN]
[ 0.40047142 0.91359586 NaN 1. ]]
>>> spearmanCorr = Statistics.corr(rdd, method="spearman")
>>> print str(spearmanCorr).replace('nan', 'NaN')
[[ 1. 0.10540926 NaN 0.4 ]
[ 0.10540926 1. NaN 0.9486833 ]
[ NaN NaN 1. NaN]
[ 0.4 0.9486833 NaN 1. ]]
>>> try:
... Statistics.corr(rdd, "spearman")
... print "Method name as second argument without 'method=' shouldn't be allowed."
... except TypeError:
... pass
"""
sc = x.ctx
# Check inputs to determine whether a single value or a matrix is needed for output.
# Since it's legal for users to use the method name as the second argument, we need to
# check if y is used to specify the method name instead.
if type(y) == str:
raise TypeError("Use 'method=' to specify method name.")
jx = _to_java_object_rdd(x)
if not y:
resultMat = sc._jvm.PythonMLLibAPI().corr(jx, method)
bytes = sc._jvm.SerDe.dumps(resultMat)
ser = PickleSerializer()
return ser.loads(str(bytes)).toArray()
else:
jy = _to_java_object_rdd(y)
return sc._jvm.PythonMLLibAPI().corr(jx, jy, method)
def test_group_by_key(self):
def gen_data(N, step):
for i in range(1, N + 1, step):
for j in range(i):
yield (i, [j])
def gen_gs(N, step=1):
return shuffle.GroupByKey(gen_data(N, step))
self.assertEqual(1, len(list(gen_gs(1))))
self.assertEqual(2, len(list(gen_gs(2))))
self.assertEqual(100, len(list(gen_gs(100))))
self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)])
self.assertTrue(
all(list(range(k)) == list(vs) for k, vs in gen_gs(100)))
for k, vs in gen_gs(50002, 10000):
self.assertEqual(k, len(vs))
self.assertEqual(list(range(k)), list(vs))
ser = PickleSerializer()
l = ser.loads(ser.dumps(list(gen_gs(50002, 30000))))
for k, vs in l:
self.assertEqual(k, len(vs))
self.assertEqual(list(range(k)), list(vs))
def corr(x, y=None, method=None):
"""
Compute the correlation (matrix) for the input RDD(s) using the
specified method.
Methods currently supported: I{pearson (default), spearman}.
If a single RDD of Vectors is passed in, a correlation matrix
comparing the columns in the input RDD is returned. Use C{method=}
to specify the method to be used for single RDD inout.
If two RDDs of floats are passed in, a single float is returned.
>>> x = sc.parallelize([1.0, 0.0, -2.0], 2)
>>> y = sc.parallelize([4.0, 5.0, 3.0], 2)
>>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2)
>>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7
True
>>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson")
True
>>> Statistics.corr(x, y, "spearman")
0.5
>>> from math import isnan
>>> isnan(Statistics.corr(x, zeros))
True
>>> from pyspark.mllib.linalg import Vectors
>>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])])
>>> pearsonCorr = Statistics.corr(rdd)
>>> print str(pearsonCorr).replace('nan', 'NaN')
[[ 1. 0.05564149 NaN 0.40047142]
[ 0.05564149 1. NaN 0.91359586]
[ NaN NaN 1. NaN]
[ 0.40047142 0.91359586 NaN 1. ]]
>>> spearmanCorr = Statistics.corr(rdd, method="spearman")
>>> print str(spearmanCorr).replace('nan', 'NaN')
[[ 1. 0.10540926 NaN 0.4 ]
[ 0.10540926 1. NaN 0.9486833 ]
[ NaN NaN 1. NaN]
[ 0.4 0.9486833 NaN 1. ]]
>>> try:
... Statistics.corr(rdd, "spearman")
... print "Method name as second argument without 'method=' shouldn't be allowed."
... except TypeError:
... pass
"""
sc = x.ctx
# Check inputs to determine whether a single value or a matrix is needed for output.
# Since it's legal for users to use the method name as the second argument, we need to
# check if y is used to specify the method name instead.
if type(y) == str:
raise TypeError("Use 'method=' to specify method name.")
jx = _to_java_object_rdd(x)
if not y:
resultMat = sc._jvm.PythonMLLibAPI().corr(jx, method)
bytes = sc._jvm.SerDe.dumps(resultMat)
ser = PickleSerializer()
return ser.loads(str(bytes)).toArray()
else:
jy = _to_java_object_rdd(y)
return sc._jvm.PythonMLLibAPI().corr(jx, jy, method)
def test_group_by_key(self):
def gen_data(N, step):
for i in range(1, N + 1, step):
for j in range(i):
yield (i, [j])
def gen_gs(N, step=1):
return shuffle.GroupByKey(gen_data(N, step))
self.assertEqual(1, len(list(gen_gs(1))))
self.assertEqual(2, len(list(gen_gs(2))))
self.assertEqual(100, len(list(gen_gs(100))))
self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)])
self.assertTrue(all(list(range(k)) == list(vs) for k, vs in gen_gs(100)))
for k, vs in gen_gs(50002, 10000):
self.assertEqual(k, len(vs))
self.assertEqual(list(range(k)), list(vs))
ser = PickleSerializer()
l = ser.loads(ser.dumps(list(gen_gs(50002, 30000))))
for k, vs in l:
self.assertEqual(k, len(vs))
self.assertEqual(list(range(k)), list(vs))
def __init__(self, vertex_jrdd, edge_jrdd,
partition_strategy=PartitionStrategy.EdgePartition1D):
self._vertex_jrdd = VertexRDD(vertex_jrdd, vertex_jrdd.context,
BatchedSerializer(PickleSerializer()))
self._edge_jrdd = EdgeRDD(edge_jrdd, edge_jrdd.context,
BatchedSerializer(PickleSerializer()))
self._partition_strategy = partition_strategy
self._jsc = vertex_jrdd.context
def _svm_to_java_object_rdd(rdd):
""" Return a JavaRDD of Object by unpickling
It will convert each Python object into Java object by Pyrolite, whenever the
RDD is serialized in batch or not.
"""
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
return rdd.ctx._jvm.org.apache.spark.mllib.api.python.LTRSerDe.pythonToJava(
rdd._jrdd, True)
def serialize(f):
ser = PickleSerializer()
@wraps(f)
def func(self):
jvec = f(self)
bytes = self._sc._jvm.SerDe.dumps(jvec)
return ser.loads(str(bytes)).toArray()
return func
def train(cls, data, lambda_=1.0):
"""
Train a Naive Bayes model given an RDD of (label, features) vectors.
This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
handle all kinds of discrete data. For example, by converting
documents into TF-IDF vectors, it can be used for document
classification. By making every vector a 0-1 vector, it can also be
used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
@param data: RDD of NumPy vectors, one per element, where the first
coordinate is the label and the rest is the feature vector
(e.g. a count vector).
@param lambda_: The smoothing parameter
"""
sc = data.context
jlist = sc._jvm.PythonMLLibAPI().trainNaiveBayes(data._to_java_object_rdd(), lambda_)
labels, pi, theta = PickleSerializer().loads(str(sc._jvm.SerDe.dumps(jlist)))
return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
def train(cls, data, lambda_=1.0):
"""
Train a Naive Bayes model given an RDD of (label, features) vectors.
This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
handle all kinds of discrete data. For example, by converting
documents into TF-IDF vectors, it can be used for document
classification. By making every vector a 0-1 vector, it can also be
used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
:param data: RDD of NumPy vectors, one per element, where the first
coordinate is the label and the rest is the feature vector
(e.g. a count vector).
:param lambda_: The smoothing parameter
"""
sc = data.context
jlist = sc._jvm.PythonMLLibAPI().trainNaiveBayes(
data._to_java_object_rdd(), lambda_)
labels, pi, theta = PickleSerializer().loads(
str(sc._jvm.SerDe.dumps(jlist)))
return NaiveBayesModel(labels.toArray(), pi.toArray(),
numpy.array(theta))
def _svm_py2java(sc, obj):
""" Convert Python object into Java """
if isinstance(obj, RDD):
obj = _svm_to_java_object_rdd(obj)
elif isinstance(obj, DataFrame):
obj = obj._jdf
elif isinstance(obj, SparkContext):
obj = obj._jsc
elif isinstance(obj, list):
obj = [_py2java(sc, x) for x in obj]
elif isinstance(obj, JavaObject):
pass
elif isinstance(obj, (int, long, float, bool, bytes, unicode)):
pass
else:
data = bytearray(PickleSerializer().dumps(obj))
obj = sc._jvm.org.apache.spark.mllib.api.python.LTRSerDe.loads(data)
return obj
def set_sc(cls,
master=None,
appName=None,
sparkHome=None,
pyFiles=None,
environment=None,
batchSize=0,
serializer=PickleSerializer(),
conf=None,
gateway=None,
jsc=None,
profiler_cls=BasicProfiler):
"""Creates and initializes a new `SparkContext` (the old one will be stopped).
Argument signature is copied from `pyspark.SparkContext
<https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.SparkContext>`_.
"""
if cls.sc is not None:
cls.sc.stop()
cls.sc = SparkContext(master, appName, sparkHome, pyFiles, environment,
batchSize, serializer, conf, gateway, jsc,
profiler_cls)
cls.__init_spark()
def __init__(self,
jrdd,
jrdd_deserializer=AutoBatchedSerializer(PickleSerializer())):
"""
Constructor
:param jrdd: A JavaRDD reference passed from the parent
RDD object
:param jrdd_deserializer: The deserializer used in Python workers
created from PythonRDD to execute a
serialized Python function and RDD
"""
self.name = "VertexRDD"
self.jrdd = jrdd
self.is_cached = False
self.is_checkpointed = False
self.ctx = SparkContext._active_spark_context
self.jvertex_rdd_deserializer = jrdd_deserializer
self.id = jrdd.id()
self.partitionFunc = None
self.bypass_serializer = False
self.preserve_partitioning = False
self.jvertex_rdd = self.getJavaVertexRDD(jrdd, jrdd_deserializer)
myAccum = sc.accumulator(3, MyAccumulatorParam())
myAccum.add(25)
print(myAccum.value) # 28
print("------------MarshalSerializer---PickleSerializer------StatusTracker--"
"---SparkJobInfo-----SparkStageInfo----")
from pyspark.serializers import FramedSerializer, CloudPickleSerializer
# MarshalSerializer,PickleSerializer 在 pyspark有快捷引入，pyspark.serializers包含所有的
# MarshalSerializer 使用Python的Marshal序列化器序列化对象 此序列化器比PickleSerializer更快，但支持的数据类型更少。
x = MarshalSerializer()
# 这个序列化器几乎支持任何Python对象，但速度可能不如更专门的序列化器快。
x2 = PickleSerializer()
# sc = SparkContext("local", "serialization app", serializer = MarshalSerializer())
# 低级状态报告API
sta = sc.statusTracker()
print(sta)
print("------")
print(sta.getActiveJobsIds()) # 返回包含所有活动作业的id的数组。
print(sta.getActiveStageIds()) # 返回包含所有活动阶段的id的数组。
print(sta.getJobIdsForGroup()
) # 返回特定作业组中所有已知作业的列表。 如果jobGroup为None，则返回未与作业组关联的所有已知作业。
# print(sta.getJobInfo(JobsId)) # 返回一个SparkJobInfo对象，如果找不到或被垃圾回收，则返回None。
# print(sta.getStageInfo(stageId)) # 返回SparkStageInfo对象，如果无法找到舞台信息或被垃圾收集，则返回None。
print("--Profiler---BasicProfiler----------")
