id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
19,000 | apache/spark | python/pyspark/rdd.py | RDD._computeFractionForSampleSize | def _computeFractionForSampleSize(sampleSizeLowerBound, total, withReplacement):
"""
Returns a sampling rate that guarantees a sample of
size >= sampleSizeLowerBound 99.99% of the time.
How the sampling rate is determined:
Let p = num / total, where num is the sample size and to... | python | def _computeFractionForSampleSize(sampleSizeLowerBound, total, withReplacement):
"""
Returns a sampling rate that guarantees a sample of
size >= sampleSizeLowerBound 99.99% of the time.
How the sampling rate is determined:
Let p = num / total, where num is the sample size and to... | [
"def",
"_computeFractionForSampleSize",
"(",
"sampleSizeLowerBound",
",",
"total",
",",
"withReplacement",
")",
":",
"fraction",
"=",
"float",
"(",
"sampleSizeLowerBound",
")",
"/",
"total",
"if",
"withReplacement",
":",
"numStDev",
"=",
"5",
"if",
"(",
"sampleSiz... | Returns a sampling rate that guarantees a sample of
size >= sampleSizeLowerBound 99.99% of the time.
How the sampling rate is determined:
Let p = num / total, where num is the sample size and total is the
total number of data points in the RDD. We're trying to compute
q > p such... | [
"Returns",
"a",
"sampling",
"rate",
"that",
"guarantees",
"a",
"sample",
"of",
"size",
">",
"=",
"sampleSizeLowerBound",
"99",
".",
"99%",
"of",
"the",
"time",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L521-L551 |
19,001 | apache/spark | python/pyspark/rdd.py | RDD.union | def union(self, other):
"""
Return the union of this RDD and another one.
>>> rdd = sc.parallelize([1, 1, 2, 3])
>>> rdd.union(rdd).collect()
[1, 1, 2, 3, 1, 1, 2, 3]
"""
if self._jrdd_deserializer == other._jrdd_deserializer:
rdd = RDD(self._jrdd.uni... | python | def union(self, other):
"""
Return the union of this RDD and another one.
>>> rdd = sc.parallelize([1, 1, 2, 3])
>>> rdd.union(rdd).collect()
[1, 1, 2, 3, 1, 1, 2, 3]
"""
if self._jrdd_deserializer == other._jrdd_deserializer:
rdd = RDD(self._jrdd.uni... | [
"def",
"union",
"(",
"self",
",",
"other",
")",
":",
"if",
"self",
".",
"_jrdd_deserializer",
"==",
"other",
".",
"_jrdd_deserializer",
":",
"rdd",
"=",
"RDD",
"(",
"self",
".",
"_jrdd",
".",
"union",
"(",
"other",
".",
"_jrdd",
")",
",",
"self",
"."... | Return the union of this RDD and another one.
>>> rdd = sc.parallelize([1, 1, 2, 3])
>>> rdd.union(rdd).collect()
[1, 1, 2, 3, 1, 1, 2, 3] | [
"Return",
"the",
"union",
"of",
"this",
"RDD",
"and",
"another",
"one",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L553-L574 |
19,002 | apache/spark | python/pyspark/rdd.py | RDD.intersection | def intersection(self, other):
"""
Return the intersection of this RDD and another one. The output will
not contain any duplicate elements, even if the input RDDs did.
.. note:: This method performs a shuffle internally.
>>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
>>... | python | def intersection(self, other):
"""
Return the intersection of this RDD and another one. The output will
not contain any duplicate elements, even if the input RDDs did.
.. note:: This method performs a shuffle internally.
>>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
>>... | [
"def",
"intersection",
"(",
"self",
",",
"other",
")",
":",
"return",
"self",
".",
"map",
"(",
"lambda",
"v",
":",
"(",
"v",
",",
"None",
")",
")",
".",
"cogroup",
"(",
"other",
".",
"map",
"(",
"lambda",
"v",
":",
"(",
"v",
",",
"None",
")",
... | Return the intersection of this RDD and another one. The output will
not contain any duplicate elements, even if the input RDDs did.
.. note:: This method performs a shuffle internally.
>>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
>>> rdd2 = sc.parallelize([1, 6, 2, 3, 7, 8])
... | [
"Return",
"the",
"intersection",
"of",
"this",
"RDD",
"and",
"another",
"one",
".",
"The",
"output",
"will",
"not",
"contain",
"any",
"duplicate",
"elements",
"even",
"if",
"the",
"input",
"RDDs",
"did",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L576-L591 |
19,003 | apache/spark | python/pyspark/rdd.py | RDD.repartitionAndSortWithinPartitions | def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=portable_hash,
ascending=True, keyfunc=lambda x: x):
"""
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their ke... | python | def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=portable_hash,
ascending=True, keyfunc=lambda x: x):
"""
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their ke... | [
"def",
"repartitionAndSortWithinPartitions",
"(",
"self",
",",
"numPartitions",
"=",
"None",
",",
"partitionFunc",
"=",
"portable_hash",
",",
"ascending",
"=",
"True",
",",
"keyfunc",
"=",
"lambda",
"x",
":",
"x",
")",
":",
"if",
"numPartitions",
"is",
"None",... | Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their keys.
>>> rdd = sc.parallelize([(0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)])
>>> rdd2 = rdd.repartitionAndSortWithinPartitions(2, lambda x: x % 2, True)
>>> rdd2.glom()... | [
"Repartition",
"the",
"RDD",
"according",
"to",
"the",
"given",
"partitioner",
"and",
"within",
"each",
"resulting",
"partition",
"sort",
"records",
"by",
"their",
"keys",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L612-L633 |
19,004 | apache/spark | python/pyspark/rdd.py | RDD.sortBy | def sortBy(self, keyfunc, ascending=True, numPartitions=None):
"""
Sorts this RDD by the given keyfunc
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
... | python | def sortBy(self, keyfunc, ascending=True, numPartitions=None):
"""
Sorts this RDD by the given keyfunc
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
... | [
"def",
"sortBy",
"(",
"self",
",",
"keyfunc",
",",
"ascending",
"=",
"True",
",",
"numPartitions",
"=",
"None",
")",
":",
"return",
"self",
".",
"keyBy",
"(",
"keyfunc",
")",
".",
"sortByKey",
"(",
"ascending",
",",
"numPartitions",
")",
".",
"values",
... | Sorts this RDD by the given keyfunc
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[1]).collect()
[('a', 1), ('b', ... | [
"Sorts",
"this",
"RDD",
"by",
"the",
"given",
"keyfunc"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L691-L701 |
19,005 | apache/spark | python/pyspark/rdd.py | RDD.groupBy | def groupBy(self, f, numPartitions=None, partitionFunc=portable_hash):
"""
Return an RDD of grouped items.
>>> rdd = sc.parallelize([1, 1, 2, 3, 5, 8])
>>> result = rdd.groupBy(lambda x: x % 2).collect()
>>> sorted([(x, sorted(y)) for (x, y) in result])
[(0, [2, 8]), (1,... | python | def groupBy(self, f, numPartitions=None, partitionFunc=portable_hash):
"""
Return an RDD of grouped items.
>>> rdd = sc.parallelize([1, 1, 2, 3, 5, 8])
>>> result = rdd.groupBy(lambda x: x % 2).collect()
>>> sorted([(x, sorted(y)) for (x, y) in result])
[(0, [2, 8]), (1,... | [
"def",
"groupBy",
"(",
"self",
",",
"f",
",",
"numPartitions",
"=",
"None",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"return",
"self",
".",
"map",
"(",
"lambda",
"x",
":",
"(",
"f",
"(",
"x",
")",
",",
"x",
")",
")",
".",
"groupByKey",... | Return an RDD of grouped items.
>>> rdd = sc.parallelize([1, 1, 2, 3, 5, 8])
>>> result = rdd.groupBy(lambda x: x % 2).collect()
>>> sorted([(x, sorted(y)) for (x, y) in result])
[(0, [2, 8]), (1, [1, 1, 3, 5])] | [
"Return",
"an",
"RDD",
"of",
"grouped",
"items",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L731-L740 |
19,006 | apache/spark | python/pyspark/rdd.py | RDD.pipe | def pipe(self, command, env=None, checkCode=False):
"""
Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the... | python | def pipe(self, command, env=None, checkCode=False):
"""
Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the... | [
"def",
"pipe",
"(",
"self",
",",
"command",
",",
"env",
"=",
"None",
",",
"checkCode",
"=",
"False",
")",
":",
"if",
"env",
"is",
"None",
":",
"env",
"=",
"dict",
"(",
")",
"def",
"func",
"(",
"iterator",
")",
":",
"pipe",
"=",
"Popen",
"(",
"s... | Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the shell command. | [
"Return",
"an",
"RDD",
"created",
"by",
"piping",
"elements",
"to",
"a",
"forked",
"external",
"process",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L743-L776 |
19,007 | apache/spark | python/pyspark/rdd.py | RDD.foreach | def foreach(self, f):
"""
Applies a function to all elements of this RDD.
>>> def f(x): print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)
"""
f = fail_on_stopiteration(f)
def processPartition(iterator):
for x in iterator:
f(x)
... | python | def foreach(self, f):
"""
Applies a function to all elements of this RDD.
>>> def f(x): print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)
"""
f = fail_on_stopiteration(f)
def processPartition(iterator):
for x in iterator:
f(x)
... | [
"def",
"foreach",
"(",
"self",
",",
"f",
")",
":",
"f",
"=",
"fail_on_stopiteration",
"(",
"f",
")",
"def",
"processPartition",
"(",
"iterator",
")",
":",
"for",
"x",
"in",
"iterator",
":",
"f",
"(",
"x",
")",
"return",
"iter",
"(",
"[",
"]",
")",
... | Applies a function to all elements of this RDD.
>>> def f(x): print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f) | [
"Applies",
"a",
"function",
"to",
"all",
"elements",
"of",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L778-L791 |
19,008 | apache/spark | python/pyspark/rdd.py | RDD.foreachPartition | def foreachPartition(self, f):
"""
Applies a function to each partition of this RDD.
>>> def f(iterator):
... for x in iterator:
... print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
"""
def func(it):
r = f(it)
... | python | def foreachPartition(self, f):
"""
Applies a function to each partition of this RDD.
>>> def f(iterator):
... for x in iterator:
... print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
"""
def func(it):
r = f(it)
... | [
"def",
"foreachPartition",
"(",
"self",
",",
"f",
")",
":",
"def",
"func",
"(",
"it",
")",
":",
"r",
"=",
"f",
"(",
"it",
")",
"try",
":",
"return",
"iter",
"(",
"r",
")",
"except",
"TypeError",
":",
"return",
"iter",
"(",
"[",
"]",
")",
"self"... | Applies a function to each partition of this RDD.
>>> def f(iterator):
... for x in iterator:
... print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f) | [
"Applies",
"a",
"function",
"to",
"each",
"partition",
"of",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L793-L808 |
19,009 | apache/spark | python/pyspark/rdd.py | RDD.collect | def collect(self):
"""
Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
"""
with SCCallSiteSync(self.context) as ... | python | def collect(self):
"""
Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
"""
with SCCallSiteSync(self.context) as ... | [
"def",
"collect",
"(",
"self",
")",
":",
"with",
"SCCallSiteSync",
"(",
"self",
".",
"context",
")",
"as",
"css",
":",
"sock_info",
"=",
"self",
".",
"ctx",
".",
"_jvm",
".",
"PythonRDD",
".",
"collectAndServe",
"(",
"self",
".",
"_jrdd",
".",
"rdd",
... | Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory. | [
"Return",
"a",
"list",
"that",
"contains",
"all",
"of",
"the",
"elements",
"in",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L810-L819 |
19,010 | apache/spark | python/pyspark/rdd.py | RDD.reduce | def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
associative binary operator. Currently reduces partitions locally.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
15
>>> sc.parallelize((2 ... | python | def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
associative binary operator. Currently reduces partitions locally.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
15
>>> sc.parallelize((2 ... | [
"def",
"reduce",
"(",
"self",
",",
"f",
")",
":",
"f",
"=",
"fail_on_stopiteration",
"(",
"f",
")",
"def",
"func",
"(",
"iterator",
")",
":",
"iterator",
"=",
"iter",
"(",
"iterator",
")",
"try",
":",
"initial",
"=",
"next",
"(",
"iterator",
")",
"... | Reduces the elements of this RDD using the specified commutative and
associative binary operator. Currently reduces partitions locally.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
15
>>> sc.parallelize((2 for _ in range(10))).map(lambda x: 1).cac... | [
"Reduces",
"the",
"elements",
"of",
"this",
"RDD",
"using",
"the",
"specified",
"commutative",
"and",
"associative",
"binary",
"operator",
".",
"Currently",
"reduces",
"partitions",
"locally",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L821-L849 |
19,011 | apache/spark | python/pyspark/rdd.py | RDD.treeReduce | def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(ad... | python | def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(ad... | [
"def",
"treeReduce",
"(",
"self",
",",
"f",
",",
"depth",
"=",
"2",
")",
":",
"if",
"depth",
"<",
"1",
":",
"raise",
"ValueError",
"(",
"\"Depth cannot be smaller than 1 but got %d.\"",
"%",
"depth",
")",
"zeroValue",
"=",
"None",
",",
"True",
"# Use the sec... | Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(add)
-5
>>> rdd.treeReduce(add, 1)
... | [
"Reduces",
"the",
"elements",
"of",
"this",
"RDD",
"in",
"a",
"multi",
"-",
"level",
"tree",
"pattern",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L851-L886 |
19,012 | apache/spark | python/pyspark/rdd.py | RDD.fold | def fold(self, zeroValue, op):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given associative function and a neutral "zero value."
The function C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avo... | python | def fold(self, zeroValue, op):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given associative function and a neutral "zero value."
The function C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avo... | [
"def",
"fold",
"(",
"self",
",",
"zeroValue",
",",
"op",
")",
":",
"op",
"=",
"fail_on_stopiteration",
"(",
"op",
")",
"def",
"func",
"(",
"iterator",
")",
":",
"acc",
"=",
"zeroValue",
"for",
"obj",
"in",
"iterator",
":",
"acc",
"=",
"op",
"(",
"a... | Aggregate the elements of each partition, and then the results for all
the partitions, using a given associative function and a neutral "zero value."
The function C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avoid object allocation; however, it should not
... | [
"Aggregate",
"the",
"elements",
"of",
"each",
"partition",
"and",
"then",
"the",
"results",
"for",
"all",
"the",
"partitions",
"using",
"a",
"given",
"associative",
"function",
"and",
"a",
"neutral",
"zero",
"value",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L888-L920 |
19,013 | apache/spark | python/pyspark/rdd.py | RDD.aggregate | def aggregate(self, zeroValue, seqOp, combOp):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given combine functions and a neutral "zero
value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
as i... | python | def aggregate(self, zeroValue, seqOp, combOp):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given combine functions and a neutral "zero
value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
as i... | [
"def",
"aggregate",
"(",
"self",
",",
"zeroValue",
",",
"seqOp",
",",
"combOp",
")",
":",
"seqOp",
"=",
"fail_on_stopiteration",
"(",
"seqOp",
")",
"combOp",
"=",
"fail_on_stopiteration",
"(",
"combOp",
")",
"def",
"func",
"(",
"iterator",
")",
":",
"acc",... | Aggregate the elements of each partition, and then the results for all
the partitions, using a given combine functions and a neutral "zero
value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avoid object allocation; however, it should not
... | [
"Aggregate",
"the",
"elements",
"of",
"each",
"partition",
"and",
"then",
"the",
"results",
"for",
"all",
"the",
"partitions",
"using",
"a",
"given",
"combine",
"functions",
"and",
"a",
"neutral",
"zero",
"value",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L922-L955 |
19,014 | apache/spark | python/pyspark/rdd.py | RDD.treeAggregate | def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
"""
Aggregates the elements of this RDD in a multi-level tree
pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, ... | python | def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
"""
Aggregates the elements of this RDD in a multi-level tree
pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, ... | [
"def",
"treeAggregate",
"(",
"self",
",",
"zeroValue",
",",
"seqOp",
",",
"combOp",
",",
"depth",
"=",
"2",
")",
":",
"if",
"depth",
"<",
"1",
":",
"raise",
"ValueError",
"(",
"\"Depth cannot be smaller than 1 but got %d.\"",
"%",
"depth",
")",
"if",
"self",... | Aggregates the elements of this RDD in a multi-level tree
pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeAggregate(0, add, add)
-5
>>> rdd.tr... | [
"Aggregates",
"the",
"elements",
"of",
"this",
"RDD",
"in",
"a",
"multi",
"-",
"level",
"tree",
"pattern",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L957-L1007 |
19,015 | apache/spark | python/pyspark/rdd.py | RDD.max | def max(self, key=None):
"""
Find the maximum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0
"""
if key is None:
... | python | def max(self, key=None):
"""
Find the maximum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0
"""
if key is None:
... | [
"def",
"max",
"(",
"self",
",",
"key",
"=",
"None",
")",
":",
"if",
"key",
"is",
"None",
":",
"return",
"self",
".",
"reduce",
"(",
"max",
")",
"return",
"self",
".",
"reduce",
"(",
"lambda",
"a",
",",
"b",
":",
"max",
"(",
"a",
",",
"b",
","... | Find the maximum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0 | [
"Find",
"the",
"maximum",
"item",
"in",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1009-L1023 |
19,016 | apache/spark | python/pyspark/rdd.py | RDD.min | def min(self, key=None):
"""
Find the minimum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
>>> rdd.min()
2.0
>>> rdd.min(key=str)
10.0
"""
if key is None:
... | python | def min(self, key=None):
"""
Find the minimum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
>>> rdd.min()
2.0
>>> rdd.min(key=str)
10.0
"""
if key is None:
... | [
"def",
"min",
"(",
"self",
",",
"key",
"=",
"None",
")",
":",
"if",
"key",
"is",
"None",
":",
"return",
"self",
".",
"reduce",
"(",
"min",
")",
"return",
"self",
".",
"reduce",
"(",
"lambda",
"a",
",",
"b",
":",
"min",
"(",
"a",
",",
"b",
","... | Find the minimum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
>>> rdd.min()
2.0
>>> rdd.min(key=str)
10.0 | [
"Find",
"the",
"minimum",
"item",
"in",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1025-L1039 |
19,017 | apache/spark | python/pyspark/rdd.py | RDD.sum | def sum(self):
"""
Add up the elements in this RDD.
>>> sc.parallelize([1.0, 2.0, 3.0]).sum()
6.0
"""
return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) | python | def sum(self):
"""
Add up the elements in this RDD.
>>> sc.parallelize([1.0, 2.0, 3.0]).sum()
6.0
"""
return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) | [
"def",
"sum",
"(",
"self",
")",
":",
"return",
"self",
".",
"mapPartitions",
"(",
"lambda",
"x",
":",
"[",
"sum",
"(",
"x",
")",
"]",
")",
".",
"fold",
"(",
"0",
",",
"operator",
".",
"add",
")"
] | Add up the elements in this RDD.
>>> sc.parallelize([1.0, 2.0, 3.0]).sum()
6.0 | [
"Add",
"up",
"the",
"elements",
"in",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1041-L1048 |
19,018 | apache/spark | python/pyspark/rdd.py | RDD.top | def top(self, num, key=None):
"""
Get the top N elements from an RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
.. note:: It returns the list sorted in descending order.
... | python | def top(self, num, key=None):
"""
Get the top N elements from an RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
.. note:: It returns the list sorted in descending order.
... | [
"def",
"top",
"(",
"self",
",",
"num",
",",
"key",
"=",
"None",
")",
":",
"def",
"topIterator",
"(",
"iterator",
")",
":",
"yield",
"heapq",
".",
"nlargest",
"(",
"num",
",",
"iterator",
",",
"key",
"=",
"key",
")",
"def",
"merge",
"(",
"a",
",",... | Get the top N elements from an RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
.. note:: It returns the list sorted in descending order.
>>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
... | [
"Get",
"the",
"top",
"N",
"elements",
"from",
"an",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1265-L1287 |
19,019 | apache/spark | python/pyspark/rdd.py | RDD.takeOrdered | def takeOrdered(self, num, key=None):
"""
Get the N elements from an RDD ordered in ascending order or as
specified by the optional key function.
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driv... | python | def takeOrdered(self, num, key=None):
"""
Get the N elements from an RDD ordered in ascending order or as
specified by the optional key function.
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driv... | [
"def",
"takeOrdered",
"(",
"self",
",",
"num",
",",
"key",
"=",
"None",
")",
":",
"def",
"merge",
"(",
"a",
",",
"b",
")",
":",
"return",
"heapq",
".",
"nsmallest",
"(",
"num",
",",
"a",
"+",
"b",
",",
"key",
")",
"return",
"self",
".",
"mapPar... | Get the N elements from an RDD ordered in ascending order or as
specified by the optional key function.
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> sc.parallelize([10, 1, 2, 9, 3, ... | [
"Get",
"the",
"N",
"elements",
"from",
"an",
"RDD",
"ordered",
"in",
"ascending",
"order",
"or",
"as",
"specified",
"by",
"the",
"optional",
"key",
"function",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1289-L1306 |
19,020 | apache/spark | python/pyspark/rdd.py | RDD.take | def take(self, num):
"""
Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RD... | python | def take(self, num):
"""
Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RD... | [
"def",
"take",
"(",
"self",
",",
"num",
")",
":",
"items",
"=",
"[",
"]",
"totalParts",
"=",
"self",
".",
"getNumPartitions",
"(",
")",
"partsScanned",
"=",
"0",
"while",
"len",
"(",
"items",
")",
"<",
"num",
"and",
"partsScanned",
"<",
"totalParts",
... | Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RDD#take().
.. note:: this method ... | [
"Take",
"the",
"first",
"num",
"elements",
"of",
"the",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1308-L1367 |
19,021 | apache/spark | python/pyspark/rdd.py | RDD.saveAsTextFile | def saveAsTextFile(self, path, compressionCodecClass=None):
"""
Save this RDD as a text file, using string representations of elements.
@param path: path to text file
@param compressionCodecClass: (None by default) string i.e.
"org.apache.hadoop.io.compress.GzipCodec"
... | python | def saveAsTextFile(self, path, compressionCodecClass=None):
"""
Save this RDD as a text file, using string representations of elements.
@param path: path to text file
@param compressionCodecClass: (None by default) string i.e.
"org.apache.hadoop.io.compress.GzipCodec"
... | [
"def",
"saveAsTextFile",
"(",
"self",
",",
"path",
",",
"compressionCodecClass",
"=",
"None",
")",
":",
"def",
"func",
"(",
"split",
",",
"iterator",
")",
":",
"for",
"x",
"in",
"iterator",
":",
"if",
"not",
"isinstance",
"(",
"x",
",",
"(",
"unicode",... | Save this RDD as a text file, using string representations of elements.
@param path: path to text file
@param compressionCodecClass: (None by default) string i.e.
"org.apache.hadoop.io.compress.GzipCodec"
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
... | [
"Save",
"this",
"RDD",
"as",
"a",
"text",
"file",
"using",
"string",
"representations",
"of",
"elements",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1524-L1572 |
19,022 | apache/spark | python/pyspark/rdd.py | RDD.reduceByKey | def reduceByKey(self, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative and commutative reduce function.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" ... | python | def reduceByKey(self, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative and commutative reduce function.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" ... | [
"def",
"reduceByKey",
"(",
"self",
",",
"func",
",",
"numPartitions",
"=",
"None",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"return",
"self",
".",
"combineByKey",
"(",
"lambda",
"x",
":",
"x",
",",
"func",
",",
"func",
",",
"numPartitions",
... | Merge the values for each key using an associative and commutative reduce function.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" in MapReduce.
Output will be partitioned with C{numPartitions} partitions, or
the... | [
"Merge",
"the",
"values",
"for",
"each",
"key",
"using",
"an",
"associative",
"and",
"commutative",
"reduce",
"function",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1611-L1627 |
19,023 | apache/spark | python/pyspark/rdd.py | RDD.reduceByKeyLocally | def reduceByKeyLocally(self, func):
"""
Merge the values for each key using an associative and commutative reduce function, but
return the results immediately to the master as a dictionary.
This will also perform the merging locally on each mapper before
sending results to a red... | python | def reduceByKeyLocally(self, func):
"""
Merge the values for each key using an associative and commutative reduce function, but
return the results immediately to the master as a dictionary.
This will also perform the merging locally on each mapper before
sending results to a red... | [
"def",
"reduceByKeyLocally",
"(",
"self",
",",
"func",
")",
":",
"func",
"=",
"fail_on_stopiteration",
"(",
"func",
")",
"def",
"reducePartition",
"(",
"iterator",
")",
":",
"m",
"=",
"{",
"}",
"for",
"k",
",",
"v",
"in",
"iterator",
":",
"m",
"[",
"... | Merge the values for each key using an associative and commutative reduce function, but
return the results immediately to the master as a dictionary.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" in MapReduce.
>... | [
"Merge",
"the",
"values",
"for",
"each",
"key",
"using",
"an",
"associative",
"and",
"commutative",
"reduce",
"function",
"but",
"return",
"the",
"results",
"immediately",
"to",
"the",
"master",
"as",
"a",
"dictionary",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1629-L1654 |
19,024 | apache/spark | python/pyspark/rdd.py | RDD.partitionBy | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[... | python | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[... | [
"def",
"partitionBy",
"(",
"self",
",",
"numPartitions",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"if",
"numPartitions",
"is",
"None",
":",
"numPartitions",
"=",
"self",
".",
"_defaultReducePartitions",
"(",
")",
"partitioner",
"=",
"Partitioner",
"... | Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[0]).intersection(set(sets[1])))
0 | [
"Return",
"a",
"copy",
"of",
"the",
"RDD",
"partitioned",
"using",
"the",
"specified",
"partitioner",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1742-L1810 |
19,025 | apache/spark | python/pyspark/rdd.py | RDD.combineByKey | def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
numPartitions=None, partitionFunc=portable_hash):
"""
Generic function to combine the elements for each key using a custom
set of aggregation functions.
Turns an RDD[(K, V)] into a result of type RDD... | python | def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
numPartitions=None, partitionFunc=portable_hash):
"""
Generic function to combine the elements for each key using a custom
set of aggregation functions.
Turns an RDD[(K, V)] into a result of type RDD... | [
"def",
"combineByKey",
"(",
"self",
",",
"createCombiner",
",",
"mergeValue",
",",
"mergeCombiners",
",",
"numPartitions",
"=",
"None",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"if",
"numPartitions",
"is",
"None",
":",
"numPartitions",
"=",
"self",
... | Generic function to combine the elements for each key using a custom
set of aggregation functions.
Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined
type" C.
Users provide three functions:
- C{createCombiner}, which turns a V into a C (e.g., creates
... | [
"Generic",
"function",
"to",
"combine",
"the",
"elements",
"for",
"each",
"key",
"using",
"a",
"custom",
"set",
"of",
"aggregation",
"functions",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1813-L1874 |
19,026 | apache/spark | python/pyspark/rdd.py | RDD.aggregateByKey | def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None,
partitionFunc=portable_hash):
"""
Aggregate the values of each key, using given combine functions and a neutral
"zero value". This function can return a different result type, U, than the type
... | python | def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None,
partitionFunc=portable_hash):
"""
Aggregate the values of each key, using given combine functions and a neutral
"zero value". This function can return a different result type, U, than the type
... | [
"def",
"aggregateByKey",
"(",
"self",
",",
"zeroValue",
",",
"seqFunc",
",",
"combFunc",
",",
"numPartitions",
"=",
"None",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"def",
"createZero",
"(",
")",
":",
"return",
"copy",
".",
"deepcopy",
"(",
"z... | Aggregate the values of each key, using given combine functions and a neutral
"zero value". This function can return a different result type, U, than the type
of the values in this RDD, V. Thus, we need one operation for merging a V into
a U and one operation for merging two U's, The former oper... | [
"Aggregate",
"the",
"values",
"of",
"each",
"key",
"using",
"given",
"combine",
"functions",
"and",
"a",
"neutral",
"zero",
"value",
".",
"This",
"function",
"can",
"return",
"a",
"different",
"result",
"type",
"U",
"than",
"the",
"type",
"of",
"the",
"val... | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1876-L1891 |
19,027 | apache/spark | python/pyspark/rdd.py | RDD.groupByKey | def groupByKey(self, numPartitions=None, partitionFunc=portable_hash):
"""
Group the values for each key in the RDD into a single sequence.
Hash-partitions the resulting RDD with numPartitions partitions.
.. note:: If you are grouping in order to perform an aggregation (such as a
... | python | def groupByKey(self, numPartitions=None, partitionFunc=portable_hash):
"""
Group the values for each key in the RDD into a single sequence.
Hash-partitions the resulting RDD with numPartitions partitions.
.. note:: If you are grouping in order to perform an aggregation (such as a
... | [
"def",
"groupByKey",
"(",
"self",
",",
"numPartitions",
"=",
"None",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"def",
"createCombiner",
"(",
"x",
")",
":",
"return",
"[",
"x",
"]",
"def",
"mergeValue",
"(",
"xs",
",",
"x",
")",
":",
"xs",
... | Group the values for each key in the RDD into a single sequence.
Hash-partitions the resulting RDD with numPartitions partitions.
.. note:: If you are grouping in order to perform an aggregation (such as a
sum or average) over each key, using reduceByKey or aggregateByKey will
p... | [
"Group",
"the",
"values",
"for",
"each",
"key",
"in",
"the",
"RDD",
"into",
"a",
"single",
"sequence",
".",
"Hash",
"-",
"partitions",
"the",
"resulting",
"RDD",
"with",
"numPartitions",
"partitions",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1915-L1958 |
19,028 | apache/spark | python/pyspark/rdd.py | RDD.flatMapValues | def flatMapValues(self, f):
"""
Pass each value in the key-value pair RDD through a flatMap function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["x", "y", "z"]), ("b", ["p", "r"])])
>>> def f(x): return x
... | python | def flatMapValues(self, f):
"""
Pass each value in the key-value pair RDD through a flatMap function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["x", "y", "z"]), ("b", ["p", "r"])])
>>> def f(x): return x
... | [
"def",
"flatMapValues",
"(",
"self",
",",
"f",
")",
":",
"flat_map_fn",
"=",
"lambda",
"kv",
":",
"(",
"(",
"kv",
"[",
"0",
"]",
",",
"x",
")",
"for",
"x",
"in",
"f",
"(",
"kv",
"[",
"1",
"]",
")",
")",
"return",
"self",
".",
"flatMap",
"(",
... | Pass each value in the key-value pair RDD through a flatMap function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["x", "y", "z"]), ("b", ["p", "r"])])
>>> def f(x): return x
>>> x.flatMapValues(f).collect()
... | [
"Pass",
"each",
"value",
"in",
"the",
"key",
"-",
"value",
"pair",
"RDD",
"through",
"a",
"flatMap",
"function",
"without",
"changing",
"the",
"keys",
";",
"this",
"also",
"retains",
"the",
"original",
"RDD",
"s",
"partitioning",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1960-L1972 |
19,029 | apache/spark | python/pyspark/rdd.py | RDD.mapValues | def mapValues(self, f):
"""
Pass each value in the key-value pair RDD through a map function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["apple", "banana", "lemon"]), ("b", ["grapes"])])
>>> def f(x): retur... | python | def mapValues(self, f):
"""
Pass each value in the key-value pair RDD through a map function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["apple", "banana", "lemon"]), ("b", ["grapes"])])
>>> def f(x): retur... | [
"def",
"mapValues",
"(",
"self",
",",
"f",
")",
":",
"map_values_fn",
"=",
"lambda",
"kv",
":",
"(",
"kv",
"[",
"0",
"]",
",",
"f",
"(",
"kv",
"[",
"1",
"]",
")",
")",
"return",
"self",
".",
"map",
"(",
"map_values_fn",
",",
"preservesPartitioning"... | Pass each value in the key-value pair RDD through a map function
without changing the keys; this also retains the original RDD's
partitioning.
>>> x = sc.parallelize([("a", ["apple", "banana", "lemon"]), ("b", ["grapes"])])
>>> def f(x): return len(x)
>>> x.mapValues(f).collect(... | [
"Pass",
"each",
"value",
"in",
"the",
"key",
"-",
"value",
"pair",
"RDD",
"through",
"a",
"map",
"function",
"without",
"changing",
"the",
"keys",
";",
"this",
"also",
"retains",
"the",
"original",
"RDD",
"s",
"partitioning",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L1974-L1986 |
19,030 | apache/spark | python/pyspark/rdd.py | RDD.coalesce | def coalesce(self, numPartitions, shuffle=False):
"""
Return a new RDD that is reduced into `numPartitions` partitions.
>>> sc.parallelize([1, 2, 3, 4, 5], 3).glom().collect()
[[1], [2, 3], [4, 5]]
>>> sc.parallelize([1, 2, 3, 4, 5], 3).coalesce(1).glom().collect()
[[1, ... | python | def coalesce(self, numPartitions, shuffle=False):
"""
Return a new RDD that is reduced into `numPartitions` partitions.
>>> sc.parallelize([1, 2, 3, 4, 5], 3).glom().collect()
[[1], [2, 3], [4, 5]]
>>> sc.parallelize([1, 2, 3, 4, 5], 3).coalesce(1).glom().collect()
[[1, ... | [
"def",
"coalesce",
"(",
"self",
",",
"numPartitions",
",",
"shuffle",
"=",
"False",
")",
":",
"if",
"shuffle",
":",
"# Decrease the batch size in order to distribute evenly the elements across output",
"# partitions. Otherwise, repartition will possibly produce highly skewed partitio... | Return a new RDD that is reduced into `numPartitions` partitions.
>>> sc.parallelize([1, 2, 3, 4, 5], 3).glom().collect()
[[1], [2, 3], [4, 5]]
>>> sc.parallelize([1, 2, 3, 4, 5], 3).coalesce(1).glom().collect()
[[1, 2, 3, 4, 5]] | [
"Return",
"a",
"new",
"RDD",
"that",
"is",
"reduced",
"into",
"numPartitions",
"partitions",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L2095-L2115 |
19,031 | apache/spark | python/pyspark/rdd.py | RDD.zipWithIndex | def zipWithIndex(self):
"""
Zips this RDD with its element indices.
The ordering is first based on the partition index and then the
ordering of items within each partition. So the first item in
the first partition gets index 0, and the last item in the last
partition rec... | python | def zipWithIndex(self):
"""
Zips this RDD with its element indices.
The ordering is first based on the partition index and then the
ordering of items within each partition. So the first item in
the first partition gets index 0, and the last item in the last
partition rec... | [
"def",
"zipWithIndex",
"(",
"self",
")",
":",
"starts",
"=",
"[",
"0",
"]",
"if",
"self",
".",
"getNumPartitions",
"(",
")",
">",
"1",
":",
"nums",
"=",
"self",
".",
"mapPartitions",
"(",
"lambda",
"it",
":",
"[",
"sum",
"(",
"1",
"for",
"i",
"in... | Zips this RDD with its element indices.
The ordering is first based on the partition index and then the
ordering of items within each partition. So the first item in
the first partition gets index 0, and the last item in the last
partition receives the largest index.
This metho... | [
"Zips",
"this",
"RDD",
"with",
"its",
"element",
"indices",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L2159-L2184 |
19,032 | apache/spark | python/pyspark/rdd.py | RDD.zipWithUniqueId | def zipWithUniqueId(self):
"""
Zips this RDD with generated unique Long ids.
Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
n is the number of partitions. So there may exist gaps, but this
method won't trigger a spark job, which is different from
L{zip... | python | def zipWithUniqueId(self):
"""
Zips this RDD with generated unique Long ids.
Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
n is the number of partitions. So there may exist gaps, but this
method won't trigger a spark job, which is different from
L{zip... | [
"def",
"zipWithUniqueId",
"(",
"self",
")",
":",
"n",
"=",
"self",
".",
"getNumPartitions",
"(",
")",
"def",
"func",
"(",
"k",
",",
"it",
")",
":",
"for",
"i",
",",
"v",
"in",
"enumerate",
"(",
"it",
")",
":",
"yield",
"v",
",",
"i",
"*",
"n",
... | Zips this RDD with generated unique Long ids.
Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
n is the number of partitions. So there may exist gaps, but this
method won't trigger a spark job, which is different from
L{zipWithIndex}
>>> sc.parallelize(["a", "b... | [
"Zips",
"this",
"RDD",
"with",
"generated",
"unique",
"Long",
"ids",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L2186-L2204 |
19,033 | apache/spark | python/pyspark/rdd.py | RDD.getStorageLevel | def getStorageLevel(self):
"""
Get the RDD's current storage level.
>>> rdd1 = sc.parallelize([1,2])
>>> rdd1.getStorageLevel()
StorageLevel(False, False, False, False, 1)
>>> print(rdd1.getStorageLevel())
Serialized 1x Replicated
"""
java_storage... | python | def getStorageLevel(self):
"""
Get the RDD's current storage level.
>>> rdd1 = sc.parallelize([1,2])
>>> rdd1.getStorageLevel()
StorageLevel(False, False, False, False, 1)
>>> print(rdd1.getStorageLevel())
Serialized 1x Replicated
"""
java_storage... | [
"def",
"getStorageLevel",
"(",
"self",
")",
":",
"java_storage_level",
"=",
"self",
".",
"_jrdd",
".",
"getStorageLevel",
"(",
")",
"storage_level",
"=",
"StorageLevel",
"(",
"java_storage_level",
".",
"useDisk",
"(",
")",
",",
"java_storage_level",
".",
"useMem... | Get the RDD's current storage level.
>>> rdd1 = sc.parallelize([1,2])
>>> rdd1.getStorageLevel()
StorageLevel(False, False, False, False, 1)
>>> print(rdd1.getStorageLevel())
Serialized 1x Replicated | [
"Get",
"the",
"RDD",
"s",
"current",
"storage",
"level",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L2234-L2250 |
19,034 | apache/spark | python/pyspark/rdd.py | RDD.lookup | def lookup(self, key):
"""
Return the list of values in the RDD for key `key`. This operation
is done efficiently if the RDD has a known partitioner by only
searching the partition that the key maps to.
>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
... | python | def lookup(self, key):
"""
Return the list of values in the RDD for key `key`. This operation
is done efficiently if the RDD has a known partitioner by only
searching the partition that the key maps to.
>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
... | [
"def",
"lookup",
"(",
"self",
",",
"key",
")",
":",
"values",
"=",
"self",
".",
"filter",
"(",
"lambda",
"kv",
":",
"kv",
"[",
"0",
"]",
"==",
"key",
")",
".",
"values",
"(",
")",
"if",
"self",
".",
"partitioner",
"is",
"not",
"None",
":",
"ret... | Return the list of values in the RDD for key `key`. This operation
is done efficiently if the RDD has a known partitioner by only
searching the partition that the key maps to.
>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
>>> rdd.lookup(42) # slow
[42]
... | [
"Return",
"the",
"list",
"of",
"values",
"in",
"the",
"RDD",
"for",
"key",
"key",
".",
"This",
"operation",
"is",
"done",
"efficiently",
"if",
"the",
"RDD",
"has",
"a",
"known",
"partitioner",
"by",
"only",
"searching",
"the",
"partition",
"that",
"the",
... | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L2267-L2291 |
19,035 | apache/spark | python/pyspark/rdd.py | RDD.toLocalIterator | def toLocalIterator(self):
"""
Return an iterator that contains all of the elements in this RDD.
The iterator will consume as much memory as the largest partition in this RDD.
>>> rdd = sc.parallelize(range(10))
>>> [x for x in rdd.toLocalIterator()]
[0, 1, 2, 3, 4, 5, 6... | python | def toLocalIterator(self):
"""
Return an iterator that contains all of the elements in this RDD.
The iterator will consume as much memory as the largest partition in this RDD.
>>> rdd = sc.parallelize(range(10))
>>> [x for x in rdd.toLocalIterator()]
[0, 1, 2, 3, 4, 5, 6... | [
"def",
"toLocalIterator",
"(",
"self",
")",
":",
"with",
"SCCallSiteSync",
"(",
"self",
".",
"context",
")",
"as",
"css",
":",
"sock_info",
"=",
"self",
".",
"ctx",
".",
"_jvm",
".",
"PythonRDD",
".",
"toLocalIteratorAndServe",
"(",
"self",
".",
"_jrdd",
... | Return an iterator that contains all of the elements in this RDD.
The iterator will consume as much memory as the largest partition in this RDD.
>>> rdd = sc.parallelize(range(10))
>>> [x for x in rdd.toLocalIterator()]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9] | [
"Return",
"an",
"iterator",
"that",
"contains",
"all",
"of",
"the",
"elements",
"in",
"this",
"RDD",
".",
"The",
"iterator",
"will",
"consume",
"as",
"much",
"memory",
"as",
"the",
"largest",
"partition",
"in",
"this",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/rdd.py#L2378-L2389 |
19,036 | apache/spark | python/pyspark/sql/column.py | _unary_op | def _unary_op(name, doc="unary operator"):
""" Create a method for given unary operator """
def _(self):
jc = getattr(self._jc, name)()
return Column(jc)
_.__doc__ = doc
return _ | python | def _unary_op(name, doc="unary operator"):
""" Create a method for given unary operator """
def _(self):
jc = getattr(self._jc, name)()
return Column(jc)
_.__doc__ = doc
return _ | [
"def",
"_unary_op",
"(",
"name",
",",
"doc",
"=",
"\"unary operator\"",
")",
":",
"def",
"_",
"(",
"self",
")",
":",
"jc",
"=",
"getattr",
"(",
"self",
".",
"_jc",
",",
"name",
")",
"(",
")",
"return",
"Column",
"(",
"jc",
")",
"_",
".",
"__doc__... | Create a method for given unary operator | [
"Create",
"a",
"method",
"for",
"given",
"unary",
"operator"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/column.py#L81-L87 |
19,037 | apache/spark | python/pyspark/sql/column.py | _bin_op | def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
def _(self, other):
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc)
_.__doc__ = doc
return _ | python | def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
def _(self, other):
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc)
_.__doc__ = doc
return _ | [
"def",
"_bin_op",
"(",
"name",
",",
"doc",
"=",
"\"binary operator\"",
")",
":",
"def",
"_",
"(",
"self",
",",
"other",
")",
":",
"jc",
"=",
"other",
".",
"_jc",
"if",
"isinstance",
"(",
"other",
",",
"Column",
")",
"else",
"other",
"njc",
"=",
"ge... | Create a method for given binary operator | [
"Create",
"a",
"method",
"for",
"given",
"binary",
"operator"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/column.py#L110-L118 |
19,038 | apache/spark | python/pyspark/sql/column.py | Column.isin | def isin(self, *cols):
"""
A boolean expression that is evaluated to true if the value of this
expression is contained by the evaluated values of the arguments.
>>> df[df.name.isin("Bob", "Mike")].collect()
[Row(age=5, name=u'Bob')]
>>> df[df.age.isin([1, 2, 3])].collect... | python | def isin(self, *cols):
"""
A boolean expression that is evaluated to true if the value of this
expression is contained by the evaluated values of the arguments.
>>> df[df.name.isin("Bob", "Mike")].collect()
[Row(age=5, name=u'Bob')]
>>> df[df.age.isin([1, 2, 3])].collect... | [
"def",
"isin",
"(",
"self",
",",
"*",
"cols",
")",
":",
"if",
"len",
"(",
"cols",
")",
"==",
"1",
"and",
"isinstance",
"(",
"cols",
"[",
"0",
"]",
",",
"(",
"list",
",",
"set",
")",
")",
":",
"cols",
"=",
"cols",
"[",
"0",
"]",
"cols",
"=",... | A boolean expression that is evaluated to true if the value of this
expression is contained by the evaluated values of the arguments.
>>> df[df.name.isin("Bob", "Mike")].collect()
[Row(age=5, name=u'Bob')]
>>> df[df.age.isin([1, 2, 3])].collect()
[Row(age=2, name=u'Alice')] | [
"A",
"boolean",
"expression",
"that",
"is",
"evaluated",
"to",
"true",
"if",
"the",
"value",
"of",
"this",
"expression",
"is",
"contained",
"by",
"the",
"evaluated",
"values",
"of",
"the",
"arguments",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/column.py#L431-L446 |
19,039 | apache/spark | python/pyspark/sql/column.py | Column.cast | def cast(self, dataType):
""" Convert the column into type ``dataType``.
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
"""
... | python | def cast(self, dataType):
""" Convert the column into type ``dataType``.
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
"""
... | [
"def",
"cast",
"(",
"self",
",",
"dataType",
")",
":",
"if",
"isinstance",
"(",
"dataType",
",",
"basestring",
")",
":",
"jc",
"=",
"self",
".",
"_jc",
".",
"cast",
"(",
"dataType",
")",
"elif",
"isinstance",
"(",
"dataType",
",",
"DataType",
")",
":... | Convert the column into type ``dataType``.
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')] | [
"Convert",
"the",
"column",
"into",
"type",
"dataType",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/column.py#L576-L593 |
19,040 | apache/spark | python/pyspark/sql/column.py | Column.over | def over(self, window):
"""
Define a windowing column.
:param window: a :class:`WindowSpec`
:return: a Column
>>> from pyspark.sql import Window
>>> window = Window.partitionBy("name").orderBy("age").rowsBetween(-1, 1)
>>> from pyspark.sql.functions import rank,... | python | def over(self, window):
"""
Define a windowing column.
:param window: a :class:`WindowSpec`
:return: a Column
>>> from pyspark.sql import Window
>>> window = Window.partitionBy("name").orderBy("age").rowsBetween(-1, 1)
>>> from pyspark.sql.functions import rank,... | [
"def",
"over",
"(",
"self",
",",
"window",
")",
":",
"from",
"pyspark",
".",
"sql",
".",
"window",
"import",
"WindowSpec",
"if",
"not",
"isinstance",
"(",
"window",
",",
"WindowSpec",
")",
":",
"raise",
"TypeError",
"(",
"\"window should be WindowSpec\"",
")... | Define a windowing column.
:param window: a :class:`WindowSpec`
:return: a Column
>>> from pyspark.sql import Window
>>> window = Window.partitionBy("name").orderBy("age").rowsBetween(-1, 1)
>>> from pyspark.sql.functions import rank, min
>>> # df.select(rank().over(win... | [
"Define",
"a",
"windowing",
"column",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/column.py#L663-L679 |
19,041 | apache/spark | python/pyspark/mllib/feature.py | StandardScaler.fit | def fit(self, dataset):
"""
Computes the mean and variance and stores as a model to be used
for later scaling.
:param dataset: The data used to compute the mean and variance
to build the transformation model.
:return: a StandardScalarModel
"""
... | python | def fit(self, dataset):
"""
Computes the mean and variance and stores as a model to be used
for later scaling.
:param dataset: The data used to compute the mean and variance
to build the transformation model.
:return: a StandardScalarModel
"""
... | [
"def",
"fit",
"(",
"self",
",",
"dataset",
")",
":",
"dataset",
"=",
"dataset",
".",
"map",
"(",
"_convert_to_vector",
")",
"jmodel",
"=",
"callMLlibFunc",
"(",
"\"fitStandardScaler\"",
",",
"self",
".",
"withMean",
",",
"self",
".",
"withStd",
",",
"datas... | Computes the mean and variance and stores as a model to be used
for later scaling.
:param dataset: The data used to compute the mean and variance
to build the transformation model.
:return: a StandardScalarModel | [
"Computes",
"the",
"mean",
"and",
"variance",
"and",
"stores",
"as",
"a",
"model",
"to",
"be",
"used",
"for",
"later",
"scaling",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/feature.py#L240-L251 |
19,042 | apache/spark | python/pyspark/mllib/feature.py | ChiSqSelector.fit | def fit(self, data):
"""
Returns a ChiSquared feature selector.
:param data: an `RDD[LabeledPoint]` containing the labeled dataset
with categorical features. Real-valued features will be
treated as categorical for each distinct value.
... | python | def fit(self, data):
"""
Returns a ChiSquared feature selector.
:param data: an `RDD[LabeledPoint]` containing the labeled dataset
with categorical features. Real-valued features will be
treated as categorical for each distinct value.
... | [
"def",
"fit",
"(",
"self",
",",
"data",
")",
":",
"jmodel",
"=",
"callMLlibFunc",
"(",
"\"fitChiSqSelector\"",
",",
"self",
".",
"selectorType",
",",
"self",
".",
"numTopFeatures",
",",
"self",
".",
"percentile",
",",
"self",
".",
"fpr",
",",
"self",
"."... | Returns a ChiSquared feature selector.
:param data: an `RDD[LabeledPoint]` containing the labeled dataset
with categorical features. Real-valued features will be
treated as categorical for each distinct value.
Apply feature discretizer before using... | [
"Returns",
"a",
"ChiSquared",
"feature",
"selector",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/feature.py#L383-L394 |
19,043 | apache/spark | python/pyspark/mllib/feature.py | IDF.fit | def fit(self, dataset):
"""
Computes the inverse document frequency.
:param dataset: an RDD of term frequency vectors
"""
if not isinstance(dataset, RDD):
raise TypeError("dataset should be an RDD of term frequency vectors")
jmodel = callMLlibFunc("fitIDF", s... | python | def fit(self, dataset):
"""
Computes the inverse document frequency.
:param dataset: an RDD of term frequency vectors
"""
if not isinstance(dataset, RDD):
raise TypeError("dataset should be an RDD of term frequency vectors")
jmodel = callMLlibFunc("fitIDF", s... | [
"def",
"fit",
"(",
"self",
",",
"dataset",
")",
":",
"if",
"not",
"isinstance",
"(",
"dataset",
",",
"RDD",
")",
":",
"raise",
"TypeError",
"(",
"\"dataset should be an RDD of term frequency vectors\"",
")",
"jmodel",
"=",
"callMLlibFunc",
"(",
"\"fitIDF\"",
","... | Computes the inverse document frequency.
:param dataset: an RDD of term frequency vectors | [
"Computes",
"the",
"inverse",
"document",
"frequency",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/feature.py#L577-L586 |
19,044 | apache/spark | python/pyspark/mllib/feature.py | Word2VecModel.findSynonyms | def findSynonyms(self, word, num):
"""
Find synonyms of a word
:param word: a word or a vector representation of word
:param num: number of synonyms to find
:return: array of (word, cosineSimilarity)
.. note:: Local use only
"""
if not isinstance(word, b... | python | def findSynonyms(self, word, num):
"""
Find synonyms of a word
:param word: a word or a vector representation of word
:param num: number of synonyms to find
:return: array of (word, cosineSimilarity)
.. note:: Local use only
"""
if not isinstance(word, b... | [
"def",
"findSynonyms",
"(",
"self",
",",
"word",
",",
"num",
")",
":",
"if",
"not",
"isinstance",
"(",
"word",
",",
"basestring",
")",
":",
"word",
"=",
"_convert_to_vector",
"(",
"word",
")",
"words",
",",
"similarity",
"=",
"self",
".",
"call",
"(",
... | Find synonyms of a word
:param word: a word or a vector representation of word
:param num: number of synonyms to find
:return: array of (word, cosineSimilarity)
.. note:: Local use only | [
"Find",
"synonyms",
"of",
"a",
"word"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/feature.py#L611-L624 |
19,045 | apache/spark | python/pyspark/mllib/feature.py | ElementwiseProduct.transform | def transform(self, vector):
"""
Computes the Hadamard product of the vector.
"""
if isinstance(vector, RDD):
vector = vector.map(_convert_to_vector)
else:
vector = _convert_to_vector(vector)
return callMLlibFunc("elementwiseProductVector", self.s... | python | def transform(self, vector):
"""
Computes the Hadamard product of the vector.
"""
if isinstance(vector, RDD):
vector = vector.map(_convert_to_vector)
else:
vector = _convert_to_vector(vector)
return callMLlibFunc("elementwiseProductVector", self.s... | [
"def",
"transform",
"(",
"self",
",",
"vector",
")",
":",
"if",
"isinstance",
"(",
"vector",
",",
"RDD",
")",
":",
"vector",
"=",
"vector",
".",
"map",
"(",
"_convert_to_vector",
")",
"else",
":",
"vector",
"=",
"_convert_to_vector",
"(",
"vector",
")",
... | Computes the Hadamard product of the vector. | [
"Computes",
"the",
"Hadamard",
"product",
"of",
"the",
"vector",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/feature.py#L810-L819 |
19,046 | apache/spark | python/pyspark/mllib/tree.py | DecisionTree.trainClassifier | def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo,
impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0):
"""
Train a decision tree model for classification.
:param data:
Training data: RDD of ... | python | def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo,
impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0):
"""
Train a decision tree model for classification.
:param data:
Training data: RDD of ... | [
"def",
"trainClassifier",
"(",
"cls",
",",
"data",
",",
"numClasses",
",",
"categoricalFeaturesInfo",
",",
"impurity",
"=",
"\"gini\"",
",",
"maxDepth",
"=",
"5",
",",
"maxBins",
"=",
"32",
",",
"minInstancesPerNode",
"=",
"1",
",",
"minInfoGain",
"=",
"0.0"... | Train a decision tree model for classification.
:param data:
Training data: RDD of LabeledPoint. Labels should take values
{0, 1, ..., numClasses-1}.
:param numClasses:
Number of classes for classification.
:param categoricalFeaturesInfo:
Map storing arit... | [
"Train",
"a",
"decision",
"tree",
"model",
"for",
"classification",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/tree.py#L149-L217 |
19,047 | apache/spark | python/pyspark/mllib/tree.py | DecisionTree.trainRegressor | def trainRegressor(cls, data, categoricalFeaturesInfo,
impurity="variance", maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0):
"""
Train a decision tree model for regression.
:param data:
Training data: RDD of LabeledPoint. L... | python | def trainRegressor(cls, data, categoricalFeaturesInfo,
impurity="variance", maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0):
"""
Train a decision tree model for regression.
:param data:
Training data: RDD of LabeledPoint. L... | [
"def",
"trainRegressor",
"(",
"cls",
",",
"data",
",",
"categoricalFeaturesInfo",
",",
"impurity",
"=",
"\"variance\"",
",",
"maxDepth",
"=",
"5",
",",
"maxBins",
"=",
"32",
",",
"minInstancesPerNode",
"=",
"1",
",",
"minInfoGain",
"=",
"0.0",
")",
":",
"r... | Train a decision tree model for regression.
:param data:
Training data: RDD of LabeledPoint. Labels are real numbers.
:param categoricalFeaturesInfo:
Map storing arity of categorical features. An entry (n -> k)
indicates that feature n is categorical with k categories
... | [
"Train",
"a",
"decision",
"tree",
"model",
"for",
"regression",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/tree.py#L221-L277 |
19,048 | apache/spark | python/pyspark/mllib/tree.py | RandomForest.trainClassifier | def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees,
featureSubsetStrategy="auto", impurity="gini", maxDepth=4, maxBins=32,
seed=None):
"""
Train a random forest model for binary or multiclass
classification.
:para... | python | def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees,
featureSubsetStrategy="auto", impurity="gini", maxDepth=4, maxBins=32,
seed=None):
"""
Train a random forest model for binary or multiclass
classification.
:para... | [
"def",
"trainClassifier",
"(",
"cls",
",",
"data",
",",
"numClasses",
",",
"categoricalFeaturesInfo",
",",
"numTrees",
",",
"featureSubsetStrategy",
"=",
"\"auto\"",
",",
"impurity",
"=",
"\"gini\"",
",",
"maxDepth",
"=",
"4",
",",
"maxBins",
"=",
"32",
",",
... | Train a random forest model for binary or multiclass
classification.
:param data:
Training dataset: RDD of LabeledPoint. Labels should take values
{0, 1, ..., numClasses-1}.
:param numClasses:
Number of classes for classification.
:param categoricalFeatures... | [
"Train",
"a",
"random",
"forest",
"model",
"for",
"binary",
"or",
"multiclass",
"classification",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/tree.py#L319-L407 |
19,049 | apache/spark | python/pyspark/mllib/tree.py | RandomForest.trainRegressor | def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy="auto",
impurity="variance", maxDepth=4, maxBins=32, seed=None):
"""
Train a random forest model for regression.
:param data:
Training dataset: RDD of LabeledPoint. Labels are... | python | def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy="auto",
impurity="variance", maxDepth=4, maxBins=32, seed=None):
"""
Train a random forest model for regression.
:param data:
Training dataset: RDD of LabeledPoint. Labels are... | [
"def",
"trainRegressor",
"(",
"cls",
",",
"data",
",",
"categoricalFeaturesInfo",
",",
"numTrees",
",",
"featureSubsetStrategy",
"=",
"\"auto\"",
",",
"impurity",
"=",
"\"variance\"",
",",
"maxDepth",
"=",
"4",
",",
"maxBins",
"=",
"32",
",",
"seed",
"=",
"N... | Train a random forest model for regression.
:param data:
Training dataset: RDD of LabeledPoint. Labels are real numbers.
:param categoricalFeaturesInfo:
Map storing arity of categorical features. An entry (n -> k)
indicates that feature n is categorical with k categories
... | [
"Train",
"a",
"random",
"forest",
"model",
"for",
"regression",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/tree.py#L411-L476 |
19,050 | apache/spark | python/pyspark/mllib/tree.py | GradientBoostedTrees.trainClassifier | def trainClassifier(cls, data, categoricalFeaturesInfo,
loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3,
maxBins=32):
"""
Train a gradient-boosted trees model for classification.
:param data:
Training dataset: RDD of Labe... | python | def trainClassifier(cls, data, categoricalFeaturesInfo,
loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3,
maxBins=32):
"""
Train a gradient-boosted trees model for classification.
:param data:
Training dataset: RDD of Labe... | [
"def",
"trainClassifier",
"(",
"cls",
",",
"data",
",",
"categoricalFeaturesInfo",
",",
"loss",
"=",
"\"logLoss\"",
",",
"numIterations",
"=",
"100",
",",
"learningRate",
"=",
"0.1",
",",
"maxDepth",
"=",
"3",
",",
"maxBins",
"=",
"32",
")",
":",
"return",... | Train a gradient-boosted trees model for classification.
:param data:
Training dataset: RDD of LabeledPoint. Labels should take values
{0, 1}.
:param categoricalFeaturesInfo:
Map storing arity of categorical features. An entry (n -> k)
indicates that feature n is... | [
"Train",
"a",
"gradient",
"-",
"boosted",
"trees",
"model",
"for",
"classification",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/tree.py#L511-L576 |
19,051 | apache/spark | python/pyspark/conf.py | SparkConf.set | def set(self, key, value):
"""Set a configuration property."""
# Try to set self._jconf first if JVM is created, set self._conf if JVM is not created yet.
if self._jconf is not None:
self._jconf.set(key, unicode(value))
else:
self._conf[key] = unicode(value)
... | python | def set(self, key, value):
"""Set a configuration property."""
# Try to set self._jconf first if JVM is created, set self._conf if JVM is not created yet.
if self._jconf is not None:
self._jconf.set(key, unicode(value))
else:
self._conf[key] = unicode(value)
... | [
"def",
"set",
"(",
"self",
",",
"key",
",",
"value",
")",
":",
"# Try to set self._jconf first if JVM is created, set self._conf if JVM is not created yet.",
"if",
"self",
".",
"_jconf",
"is",
"not",
"None",
":",
"self",
".",
"_jconf",
".",
"set",
"(",
"key",
",",... | Set a configuration property. | [
"Set",
"a",
"configuration",
"property",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L123-L130 |
19,052 | apache/spark | python/pyspark/conf.py | SparkConf.setIfMissing | def setIfMissing(self, key, value):
"""Set a configuration property, if not already set."""
if self.get(key) is None:
self.set(key, value)
return self | python | def setIfMissing(self, key, value):
"""Set a configuration property, if not already set."""
if self.get(key) is None:
self.set(key, value)
return self | [
"def",
"setIfMissing",
"(",
"self",
",",
"key",
",",
"value",
")",
":",
"if",
"self",
".",
"get",
"(",
"key",
")",
"is",
"None",
":",
"self",
".",
"set",
"(",
"key",
",",
"value",
")",
"return",
"self"
] | Set a configuration property, if not already set. | [
"Set",
"a",
"configuration",
"property",
"if",
"not",
"already",
"set",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L132-L136 |
19,053 | apache/spark | python/pyspark/conf.py | SparkConf.setExecutorEnv | def setExecutorEnv(self, key=None, value=None, pairs=None):
"""Set an environment variable to be passed to executors."""
if (key is not None and pairs is not None) or (key is None and pairs is None):
raise Exception("Either pass one key-value pair or a list of pairs")
elif key is not... | python | def setExecutorEnv(self, key=None, value=None, pairs=None):
"""Set an environment variable to be passed to executors."""
if (key is not None and pairs is not None) or (key is None and pairs is None):
raise Exception("Either pass one key-value pair or a list of pairs")
elif key is not... | [
"def",
"setExecutorEnv",
"(",
"self",
",",
"key",
"=",
"None",
",",
"value",
"=",
"None",
",",
"pairs",
"=",
"None",
")",
":",
"if",
"(",
"key",
"is",
"not",
"None",
"and",
"pairs",
"is",
"not",
"None",
")",
"or",
"(",
"key",
"is",
"None",
"and",... | Set an environment variable to be passed to executors. | [
"Set",
"an",
"environment",
"variable",
"to",
"be",
"passed",
"to",
"executors",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L153-L162 |
19,054 | apache/spark | python/pyspark/conf.py | SparkConf.setAll | def setAll(self, pairs):
"""
Set multiple parameters, passed as a list of key-value pairs.
:param pairs: list of key-value pairs to set
"""
for (k, v) in pairs:
self.set(k, v)
return self | python | def setAll(self, pairs):
"""
Set multiple parameters, passed as a list of key-value pairs.
:param pairs: list of key-value pairs to set
"""
for (k, v) in pairs:
self.set(k, v)
return self | [
"def",
"setAll",
"(",
"self",
",",
"pairs",
")",
":",
"for",
"(",
"k",
",",
"v",
")",
"in",
"pairs",
":",
"self",
".",
"set",
"(",
"k",
",",
"v",
")",
"return",
"self"
] | Set multiple parameters, passed as a list of key-value pairs.
:param pairs: list of key-value pairs to set | [
"Set",
"multiple",
"parameters",
"passed",
"as",
"a",
"list",
"of",
"key",
"-",
"value",
"pairs",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L164-L172 |
19,055 | apache/spark | python/pyspark/conf.py | SparkConf.get | def get(self, key, defaultValue=None):
"""Get the configured value for some key, or return a default otherwise."""
if defaultValue is None: # Py4J doesn't call the right get() if we pass None
if self._jconf is not None:
if not self._jconf.contains(key):
... | python | def get(self, key, defaultValue=None):
"""Get the configured value for some key, or return a default otherwise."""
if defaultValue is None: # Py4J doesn't call the right get() if we pass None
if self._jconf is not None:
if not self._jconf.contains(key):
... | [
"def",
"get",
"(",
"self",
",",
"key",
",",
"defaultValue",
"=",
"None",
")",
":",
"if",
"defaultValue",
"is",
"None",
":",
"# Py4J doesn't call the right get() if we pass None",
"if",
"self",
".",
"_jconf",
"is",
"not",
"None",
":",
"if",
"not",
"self",
"."... | Get the configured value for some key, or return a default otherwise. | [
"Get",
"the",
"configured",
"value",
"for",
"some",
"key",
"or",
"return",
"a",
"default",
"otherwise",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L174-L189 |
19,056 | apache/spark | python/pyspark/conf.py | SparkConf.getAll | def getAll(self):
"""Get all values as a list of key-value pairs."""
if self._jconf is not None:
return [(elem._1(), elem._2()) for elem in self._jconf.getAll()]
else:
return self._conf.items() | python | def getAll(self):
"""Get all values as a list of key-value pairs."""
if self._jconf is not None:
return [(elem._1(), elem._2()) for elem in self._jconf.getAll()]
else:
return self._conf.items() | [
"def",
"getAll",
"(",
"self",
")",
":",
"if",
"self",
".",
"_jconf",
"is",
"not",
"None",
":",
"return",
"[",
"(",
"elem",
".",
"_1",
"(",
")",
",",
"elem",
".",
"_2",
"(",
")",
")",
"for",
"elem",
"in",
"self",
".",
"_jconf",
".",
"getAll",
... | Get all values as a list of key-value pairs. | [
"Get",
"all",
"values",
"as",
"a",
"list",
"of",
"key",
"-",
"value",
"pairs",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L191-L196 |
19,057 | apache/spark | python/pyspark/conf.py | SparkConf.contains | def contains(self, key):
"""Does this configuration contain a given key?"""
if self._jconf is not None:
return self._jconf.contains(key)
else:
return key in self._conf | python | def contains(self, key):
"""Does this configuration contain a given key?"""
if self._jconf is not None:
return self._jconf.contains(key)
else:
return key in self._conf | [
"def",
"contains",
"(",
"self",
",",
"key",
")",
":",
"if",
"self",
".",
"_jconf",
"is",
"not",
"None",
":",
"return",
"self",
".",
"_jconf",
".",
"contains",
"(",
"key",
")",
"else",
":",
"return",
"key",
"in",
"self",
".",
"_conf"
] | Does this configuration contain a given key? | [
"Does",
"this",
"configuration",
"contain",
"a",
"given",
"key?"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L198-L203 |
19,058 | apache/spark | python/pyspark/conf.py | SparkConf.toDebugString | def toDebugString(self):
"""
Returns a printable version of the configuration, as a list of
key=value pairs, one per line.
"""
if self._jconf is not None:
return self._jconf.toDebugString()
else:
return '\n'.join('%s=%s' % (k, v) for k, v in self._... | python | def toDebugString(self):
"""
Returns a printable version of the configuration, as a list of
key=value pairs, one per line.
"""
if self._jconf is not None:
return self._jconf.toDebugString()
else:
return '\n'.join('%s=%s' % (k, v) for k, v in self._... | [
"def",
"toDebugString",
"(",
"self",
")",
":",
"if",
"self",
".",
"_jconf",
"is",
"not",
"None",
":",
"return",
"self",
".",
"_jconf",
".",
"toDebugString",
"(",
")",
"else",
":",
"return",
"'\\n'",
".",
"join",
"(",
"'%s=%s'",
"%",
"(",
"k",
",",
... | Returns a printable version of the configuration, as a list of
key=value pairs, one per line. | [
"Returns",
"a",
"printable",
"version",
"of",
"the",
"configuration",
"as",
"a",
"list",
"of",
"key",
"=",
"value",
"pairs",
"one",
"per",
"line",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/conf.py#L205-L213 |
19,059 | apache/spark | python/pyspark/sql/catalog.py | Catalog.listDatabases | def listDatabases(self):
"""Returns a list of databases available across all sessions."""
iter = self._jcatalog.listDatabases().toLocalIterator()
databases = []
while iter.hasNext():
jdb = iter.next()
databases.append(Database(
name=jdb.name(),
... | python | def listDatabases(self):
"""Returns a list of databases available across all sessions."""
iter = self._jcatalog.listDatabases().toLocalIterator()
databases = []
while iter.hasNext():
jdb = iter.next()
databases.append(Database(
name=jdb.name(),
... | [
"def",
"listDatabases",
"(",
"self",
")",
":",
"iter",
"=",
"self",
".",
"_jcatalog",
".",
"listDatabases",
"(",
")",
".",
"toLocalIterator",
"(",
")",
"databases",
"=",
"[",
"]",
"while",
"iter",
".",
"hasNext",
"(",
")",
":",
"jdb",
"=",
"iter",
".... | Returns a list of databases available across all sessions. | [
"Returns",
"a",
"list",
"of",
"databases",
"available",
"across",
"all",
"sessions",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/catalog.py#L61-L71 |
19,060 | apache/spark | python/pyspark/sql/catalog.py | Catalog.listFunctions | def listFunctions(self, dbName=None):
"""Returns a list of functions registered in the specified database.
If no database is specified, the current database is used.
This includes all temporary functions.
"""
if dbName is None:
dbName = self.currentDatabase()
... | python | def listFunctions(self, dbName=None):
"""Returns a list of functions registered in the specified database.
If no database is specified, the current database is used.
This includes all temporary functions.
"""
if dbName is None:
dbName = self.currentDatabase()
... | [
"def",
"listFunctions",
"(",
"self",
",",
"dbName",
"=",
"None",
")",
":",
"if",
"dbName",
"is",
"None",
":",
"dbName",
"=",
"self",
".",
"currentDatabase",
"(",
")",
"iter",
"=",
"self",
".",
"_jcatalog",
".",
"listFunctions",
"(",
"dbName",
")",
".",... | Returns a list of functions registered in the specified database.
If no database is specified, the current database is used.
This includes all temporary functions. | [
"Returns",
"a",
"list",
"of",
"functions",
"registered",
"in",
"the",
"specified",
"database",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/sql/catalog.py#L97-L114 |
19,061 | apache/spark | python/pyspark/taskcontext.py | _load_from_socket | def _load_from_socket(port, auth_secret):
"""
Load data from a given socket, this is a blocking method thus only return when the socket
connection has been closed.
"""
(sockfile, sock) = local_connect_and_auth(port, auth_secret)
# The barrier() call may block forever, so no timeout
sock.sett... | python | def _load_from_socket(port, auth_secret):
"""
Load data from a given socket, this is a blocking method thus only return when the socket
connection has been closed.
"""
(sockfile, sock) = local_connect_and_auth(port, auth_secret)
# The barrier() call may block forever, so no timeout
sock.sett... | [
"def",
"_load_from_socket",
"(",
"port",
",",
"auth_secret",
")",
":",
"(",
"sockfile",
",",
"sock",
")",
"=",
"local_connect_and_auth",
"(",
"port",
",",
"auth_secret",
")",
"# The barrier() call may block forever, so no timeout",
"sock",
".",
"settimeout",
"(",
"N... | Load data from a given socket, this is a blocking method thus only return when the socket
connection has been closed. | [
"Load",
"data",
"from",
"a",
"given",
"socket",
"this",
"is",
"a",
"blocking",
"method",
"thus",
"only",
"return",
"when",
"the",
"socket",
"connection",
"has",
"been",
"closed",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/taskcontext.py#L102-L121 |
19,062 | apache/spark | python/pyspark/taskcontext.py | BarrierTaskContext._getOrCreate | def _getOrCreate(cls):
"""
Internal function to get or create global BarrierTaskContext. We need to make sure
BarrierTaskContext is returned from here because it is needed in python worker reuse
scenario, see SPARK-25921 for more details.
"""
if not isinstance(cls._taskCo... | python | def _getOrCreate(cls):
"""
Internal function to get or create global BarrierTaskContext. We need to make sure
BarrierTaskContext is returned from here because it is needed in python worker reuse
scenario, see SPARK-25921 for more details.
"""
if not isinstance(cls._taskCo... | [
"def",
"_getOrCreate",
"(",
"cls",
")",
":",
"if",
"not",
"isinstance",
"(",
"cls",
".",
"_taskContext",
",",
"BarrierTaskContext",
")",
":",
"cls",
".",
"_taskContext",
"=",
"object",
".",
"__new__",
"(",
"cls",
")",
"return",
"cls",
".",
"_taskContext"
] | Internal function to get or create global BarrierTaskContext. We need to make sure
BarrierTaskContext is returned from here because it is needed in python worker reuse
scenario, see SPARK-25921 for more details. | [
"Internal",
"function",
"to",
"get",
"or",
"create",
"global",
"BarrierTaskContext",
".",
"We",
"need",
"to",
"make",
"sure",
"BarrierTaskContext",
"is",
"returned",
"from",
"here",
"because",
"it",
"is",
"needed",
"in",
"python",
"worker",
"reuse",
"scenario",
... | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/taskcontext.py#L139-L147 |
19,063 | apache/spark | python/pyspark/taskcontext.py | BarrierTaskContext._initialize | def _initialize(cls, port, secret):
"""
Initialize BarrierTaskContext, other methods within BarrierTaskContext can only be called
after BarrierTaskContext is initialized.
"""
cls._port = port
cls._secret = secret | python | def _initialize(cls, port, secret):
"""
Initialize BarrierTaskContext, other methods within BarrierTaskContext can only be called
after BarrierTaskContext is initialized.
"""
cls._port = port
cls._secret = secret | [
"def",
"_initialize",
"(",
"cls",
",",
"port",
",",
"secret",
")",
":",
"cls",
".",
"_port",
"=",
"port",
"cls",
".",
"_secret",
"=",
"secret"
] | Initialize BarrierTaskContext, other methods within BarrierTaskContext can only be called
after BarrierTaskContext is initialized. | [
"Initialize",
"BarrierTaskContext",
"other",
"methods",
"within",
"BarrierTaskContext",
"can",
"only",
"be",
"called",
"after",
"BarrierTaskContext",
"is",
"initialized",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/taskcontext.py#L163-L169 |
19,064 | apache/spark | python/pyspark/__init__.py | since | def since(version):
"""
A decorator that annotates a function to append the version of Spark the function was added.
"""
import re
indent_p = re.compile(r'\n( +)')
def deco(f):
indents = indent_p.findall(f.__doc__)
indent = ' ' * (min(len(m) for m in indents) if indents else 0)
... | python | def since(version):
"""
A decorator that annotates a function to append the version of Spark the function was added.
"""
import re
indent_p = re.compile(r'\n( +)')
def deco(f):
indents = indent_p.findall(f.__doc__)
indent = ' ' * (min(len(m) for m in indents) if indents else 0)
... | [
"def",
"since",
"(",
"version",
")",
":",
"import",
"re",
"indent_p",
"=",
"re",
".",
"compile",
"(",
"r'\\n( +)'",
")",
"def",
"deco",
"(",
"f",
")",
":",
"indents",
"=",
"indent_p",
".",
"findall",
"(",
"f",
".",
"__doc__",
")",
"indent",
"=",
"'... | A decorator that annotates a function to append the version of Spark the function was added. | [
"A",
"decorator",
"that",
"annotates",
"a",
"function",
"to",
"append",
"the",
"version",
"of",
"Spark",
"the",
"function",
"was",
"added",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/__init__.py#L65-L77 |
19,065 | apache/spark | python/pyspark/__init__.py | keyword_only | def keyword_only(func):
"""
A decorator that forces keyword arguments in the wrapped method
and saves actual input keyword arguments in `_input_kwargs`.
.. note:: Should only be used to wrap a method where first arg is `self`
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if l... | python | def keyword_only(func):
"""
A decorator that forces keyword arguments in the wrapped method
and saves actual input keyword arguments in `_input_kwargs`.
.. note:: Should only be used to wrap a method where first arg is `self`
"""
@wraps(func)
def wrapper(self, *args, **kwargs):
if l... | [
"def",
"keyword_only",
"(",
"func",
")",
":",
"@",
"wraps",
"(",
"func",
")",
"def",
"wrapper",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"len",
"(",
"args",
")",
">",
"0",
":",
"raise",
"TypeError",
"(",
"\"Method %... | A decorator that forces keyword arguments in the wrapped method
and saves actual input keyword arguments in `_input_kwargs`.
.. note:: Should only be used to wrap a method where first arg is `self` | [
"A",
"decorator",
"that",
"forces",
"keyword",
"arguments",
"in",
"the",
"wrapped",
"method",
"and",
"saves",
"actual",
"input",
"keyword",
"arguments",
"in",
"_input_kwargs",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/__init__.py#L98-L111 |
19,066 | apache/spark | python/pyspark/ml/param/_shared_params_code_gen.py | _gen_param_code | def _gen_param_code(name, doc, defaultValueStr):
"""
Generates Python code for a shared param class.
:param name: param name
:param doc: param doc
:param defaultValueStr: string representation of the default value
:return: code string
"""
# TODO: How to correctly inherit instance attrib... | python | def _gen_param_code(name, doc, defaultValueStr):
"""
Generates Python code for a shared param class.
:param name: param name
:param doc: param doc
:param defaultValueStr: string representation of the default value
:return: code string
"""
# TODO: How to correctly inherit instance attrib... | [
"def",
"_gen_param_code",
"(",
"name",
",",
"doc",
",",
"defaultValueStr",
")",
":",
"# TODO: How to correctly inherit instance attributes?",
"template",
"=",
"'''\n def set$Name(self, value):\n \"\"\"\n Sets the value of :py:attr:`$name`.\n \"\"\"\n return s... | Generates Python code for a shared param class.
:param name: param name
:param doc: param doc
:param defaultValueStr: string representation of the default value
:return: code string | [
"Generates",
"Python",
"code",
"for",
"a",
"shared",
"param",
"class",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/ml/param/_shared_params_code_gen.py#L73-L101 |
19,067 | apache/spark | python/pyspark/mllib/clustering.py | BisectingKMeans.train | def train(self, rdd, k=4, maxIterations=20, minDivisibleClusterSize=1.0, seed=-1888008604):
"""
Runs the bisecting k-means algorithm return the model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
The desired n... | python | def train(self, rdd, k=4, maxIterations=20, minDivisibleClusterSize=1.0, seed=-1888008604):
"""
Runs the bisecting k-means algorithm return the model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
The desired n... | [
"def",
"train",
"(",
"self",
",",
"rdd",
",",
"k",
"=",
"4",
",",
"maxIterations",
"=",
"20",
",",
"minDivisibleClusterSize",
"=",
"1.0",
",",
"seed",
"=",
"-",
"1888008604",
")",
":",
"java_model",
"=",
"callMLlibFunc",
"(",
"\"trainBisectingKMeans\"",
",... | Runs the bisecting k-means algorithm return the model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
The desired number of leaf clusters. The actual number could
be smaller if there are no divisible leaf clusters.
... | [
"Runs",
"the",
"bisecting",
"k",
"-",
"means",
"algorithm",
"return",
"the",
"model",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L142-L167 |
19,068 | apache/spark | python/pyspark/mllib/clustering.py | KMeans.train | def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
seed=None, initializationSteps=2, epsilon=1e-4, initialModel=None):
"""
Train a k-means clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequen... | python | def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
seed=None, initializationSteps=2, epsilon=1e-4, initialModel=None):
"""
Train a k-means clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequen... | [
"def",
"train",
"(",
"cls",
",",
"rdd",
",",
"k",
",",
"maxIterations",
"=",
"100",
",",
"runs",
"=",
"1",
",",
"initializationMode",
"=",
"\"k-means||\"",
",",
"seed",
"=",
"None",
",",
"initializationSteps",
"=",
"2",
",",
"epsilon",
"=",
"1e-4",
","... | Train a k-means clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of clusters to create.
:param maxIterations:
Maximum number of iterations allowed.
(default: 100)
:para... | [
"Train",
"a",
"k",
"-",
"means",
"clustering",
"model",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L307-L357 |
19,069 | apache/spark | python/pyspark/mllib/clustering.py | GaussianMixture.train | def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
"""
Train a Gaussian Mixture clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of independent G... | python | def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initialModel=None):
"""
Train a Gaussian Mixture clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of independent G... | [
"def",
"train",
"(",
"cls",
",",
"rdd",
",",
"k",
",",
"convergenceTol",
"=",
"1e-3",
",",
"maxIterations",
"=",
"100",
",",
"seed",
"=",
"None",
",",
"initialModel",
"=",
"None",
")",
":",
"initialModelWeights",
"=",
"None",
"initialModelMu",
"=",
"None... | Train a Gaussian Mixture clustering model.
:param rdd:
Training points as an `RDD` of `Vector` or convertible
sequence types.
:param k:
Number of independent Gaussians in the mixture model.
:param convergenceTol:
Maximum change in log-likelihood at which ... | [
"Train",
"a",
"Gaussian",
"Mixture",
"clustering",
"model",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L515-L553 |
19,070 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeansModel.update | def update(self, data, decayFactor, timeUnit):
"""Update the centroids, according to data
:param data:
RDD with new data for the model update.
:param decayFactor:
Forgetfulness of the previous centroids.
:param timeUnit:
Can be "batches" or "points". If poi... | python | def update(self, data, decayFactor, timeUnit):
"""Update the centroids, according to data
:param data:
RDD with new data for the model update.
:param decayFactor:
Forgetfulness of the previous centroids.
:param timeUnit:
Can be "batches" or "points". If poi... | [
"def",
"update",
"(",
"self",
",",
"data",
",",
"decayFactor",
",",
"timeUnit",
")",
":",
"if",
"not",
"isinstance",
"(",
"data",
",",
"RDD",
")",
":",
"raise",
"TypeError",
"(",
"\"Data should be of an RDD, got %s.\"",
"%",
"type",
"(",
"data",
")",
")",
... | Update the centroids, according to data
:param data:
RDD with new data for the model update.
:param decayFactor:
Forgetfulness of the previous centroids.
:param timeUnit:
Can be "batches" or "points". If points, then the decay factor
is raised to the powe... | [
"Update",
"the",
"centroids",
"according",
"to",
"data"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L752-L777 |
19,071 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeans.setHalfLife | def setHalfLife(self, halfLife, timeUnit):
"""
Set number of batches after which the centroids of that
particular batch has half the weightage.
"""
self._timeUnit = timeUnit
self._decayFactor = exp(log(0.5) / halfLife)
return self | python | def setHalfLife(self, halfLife, timeUnit):
"""
Set number of batches after which the centroids of that
particular batch has half the weightage.
"""
self._timeUnit = timeUnit
self._decayFactor = exp(log(0.5) / halfLife)
return self | [
"def",
"setHalfLife",
"(",
"self",
",",
"halfLife",
",",
"timeUnit",
")",
":",
"self",
".",
"_timeUnit",
"=",
"timeUnit",
"self",
".",
"_decayFactor",
"=",
"exp",
"(",
"log",
"(",
"0.5",
")",
"/",
"halfLife",
")",
"return",
"self"
] | Set number of batches after which the centroids of that
particular batch has half the weightage. | [
"Set",
"number",
"of",
"batches",
"after",
"which",
"the",
"centroids",
"of",
"that",
"particular",
"batch",
"has",
"half",
"the",
"weightage",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L838-L845 |
19,072 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeans.setInitialCenters | def setInitialCenters(self, centers, weights):
"""
Set initial centers. Should be set before calling trainOn.
"""
self._model = StreamingKMeansModel(centers, weights)
return self | python | def setInitialCenters(self, centers, weights):
"""
Set initial centers. Should be set before calling trainOn.
"""
self._model = StreamingKMeansModel(centers, weights)
return self | [
"def",
"setInitialCenters",
"(",
"self",
",",
"centers",
",",
"weights",
")",
":",
"self",
".",
"_model",
"=",
"StreamingKMeansModel",
"(",
"centers",
",",
"weights",
")",
"return",
"self"
] | Set initial centers. Should be set before calling trainOn. | [
"Set",
"initial",
"centers",
".",
"Should",
"be",
"set",
"before",
"calling",
"trainOn",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L848-L853 |
19,073 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeans.setRandomCenters | def setRandomCenters(self, dim, weight, seed):
"""
Set the initial centres to be random samples from
a gaussian population with constant weights.
"""
rng = random.RandomState(seed)
clusterCenters = rng.randn(self._k, dim)
clusterWeights = tile(weight, self._k)
... | python | def setRandomCenters(self, dim, weight, seed):
"""
Set the initial centres to be random samples from
a gaussian population with constant weights.
"""
rng = random.RandomState(seed)
clusterCenters = rng.randn(self._k, dim)
clusterWeights = tile(weight, self._k)
... | [
"def",
"setRandomCenters",
"(",
"self",
",",
"dim",
",",
"weight",
",",
"seed",
")",
":",
"rng",
"=",
"random",
".",
"RandomState",
"(",
"seed",
")",
"clusterCenters",
"=",
"rng",
".",
"randn",
"(",
"self",
".",
"_k",
",",
"dim",
")",
"clusterWeights",... | Set the initial centres to be random samples from
a gaussian population with constant weights. | [
"Set",
"the",
"initial",
"centres",
"to",
"be",
"random",
"samples",
"from",
"a",
"gaussian",
"population",
"with",
"constant",
"weights",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L856-L865 |
19,074 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeans.trainOn | def trainOn(self, dstream):
"""Train the model on the incoming dstream."""
self._validate(dstream)
def update(rdd):
self._model.update(rdd, self._decayFactor, self._timeUnit)
dstream.foreachRDD(update) | python | def trainOn(self, dstream):
"""Train the model on the incoming dstream."""
self._validate(dstream)
def update(rdd):
self._model.update(rdd, self._decayFactor, self._timeUnit)
dstream.foreachRDD(update) | [
"def",
"trainOn",
"(",
"self",
",",
"dstream",
")",
":",
"self",
".",
"_validate",
"(",
"dstream",
")",
"def",
"update",
"(",
"rdd",
")",
":",
"self",
".",
"_model",
".",
"update",
"(",
"rdd",
",",
"self",
".",
"_decayFactor",
",",
"self",
".",
"_t... | Train the model on the incoming dstream. | [
"Train",
"the",
"model",
"on",
"the",
"incoming",
"dstream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L868-L875 |
19,075 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeans.predictOn | def predictOn(self, dstream):
"""
Make predictions on a dstream.
Returns a transformed dstream object
"""
self._validate(dstream)
return dstream.map(lambda x: self._model.predict(x)) | python | def predictOn(self, dstream):
"""
Make predictions on a dstream.
Returns a transformed dstream object
"""
self._validate(dstream)
return dstream.map(lambda x: self._model.predict(x)) | [
"def",
"predictOn",
"(",
"self",
",",
"dstream",
")",
":",
"self",
".",
"_validate",
"(",
"dstream",
")",
"return",
"dstream",
".",
"map",
"(",
"lambda",
"x",
":",
"self",
".",
"_model",
".",
"predict",
"(",
"x",
")",
")"
] | Make predictions on a dstream.
Returns a transformed dstream object | [
"Make",
"predictions",
"on",
"a",
"dstream",
".",
"Returns",
"a",
"transformed",
"dstream",
"object"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L878-L884 |
19,076 | apache/spark | python/pyspark/mllib/clustering.py | StreamingKMeans.predictOnValues | def predictOnValues(self, dstream):
"""
Make predictions on a keyed dstream.
Returns a transformed dstream object.
"""
self._validate(dstream)
return dstream.mapValues(lambda x: self._model.predict(x)) | python | def predictOnValues(self, dstream):
"""
Make predictions on a keyed dstream.
Returns a transformed dstream object.
"""
self._validate(dstream)
return dstream.mapValues(lambda x: self._model.predict(x)) | [
"def",
"predictOnValues",
"(",
"self",
",",
"dstream",
")",
":",
"self",
".",
"_validate",
"(",
"dstream",
")",
"return",
"dstream",
".",
"mapValues",
"(",
"lambda",
"x",
":",
"self",
".",
"_model",
".",
"predict",
"(",
"x",
")",
")"
] | Make predictions on a keyed dstream.
Returns a transformed dstream object. | [
"Make",
"predictions",
"on",
"a",
"keyed",
"dstream",
".",
"Returns",
"a",
"transformed",
"dstream",
"object",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L887-L893 |
19,077 | apache/spark | python/pyspark/mllib/clustering.py | LDAModel.describeTopics | def describeTopics(self, maxTermsPerTopic=None):
"""Return the topics described by weighted terms.
WARNING: If vocabSize and k are large, this can return a large object!
:param maxTermsPerTopic:
Maximum number of terms to collect for each topic.
(default: vocabulary size)
... | python | def describeTopics(self, maxTermsPerTopic=None):
"""Return the topics described by weighted terms.
WARNING: If vocabSize and k are large, this can return a large object!
:param maxTermsPerTopic:
Maximum number of terms to collect for each topic.
(default: vocabulary size)
... | [
"def",
"describeTopics",
"(",
"self",
",",
"maxTermsPerTopic",
"=",
"None",
")",
":",
"if",
"maxTermsPerTopic",
"is",
"None",
":",
"topics",
"=",
"self",
".",
"call",
"(",
"\"describeTopics\"",
")",
"else",
":",
"topics",
"=",
"self",
".",
"call",
"(",
"... | Return the topics described by weighted terms.
WARNING: If vocabSize and k are large, this can return a large object!
:param maxTermsPerTopic:
Maximum number of terms to collect for each topic.
(default: vocabulary size)
:return:
Array over topics. Each topic is r... | [
"Return",
"the",
"topics",
"described",
"by",
"weighted",
"terms",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L955-L972 |
19,078 | apache/spark | python/pyspark/mllib/clustering.py | LDAModel.load | def load(cls, sc, path):
"""Load the LDAModel from disk.
:param sc:
SparkContext.
:param path:
Path to where the model is stored.
"""
if not isinstance(sc, SparkContext):
raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
... | python | def load(cls, sc, path):
"""Load the LDAModel from disk.
:param sc:
SparkContext.
:param path:
Path to where the model is stored.
"""
if not isinstance(sc, SparkContext):
raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
... | [
"def",
"load",
"(",
"cls",
",",
"sc",
",",
"path",
")",
":",
"if",
"not",
"isinstance",
"(",
"sc",
",",
"SparkContext",
")",
":",
"raise",
"TypeError",
"(",
"\"sc should be a SparkContext, got type %s\"",
"%",
"type",
"(",
"sc",
")",
")",
"if",
"not",
"i... | Load the LDAModel from disk.
:param sc:
SparkContext.
:param path:
Path to where the model is stored. | [
"Load",
"the",
"LDAModel",
"from",
"disk",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L976-L989 |
19,079 | apache/spark | python/pyspark/mllib/clustering.py | LDA.train | def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0,
topicConcentration=-1.0, seed=None, checkpointInterval=10, optimizer="em"):
"""Train a LDA model.
:param rdd:
RDD of documents, which are tuples of document IDs and term
(word) count vectors. The term c... | python | def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0,
topicConcentration=-1.0, seed=None, checkpointInterval=10, optimizer="em"):
"""Train a LDA model.
:param rdd:
RDD of documents, which are tuples of document IDs and term
(word) count vectors. The term c... | [
"def",
"train",
"(",
"cls",
",",
"rdd",
",",
"k",
"=",
"10",
",",
"maxIterations",
"=",
"20",
",",
"docConcentration",
"=",
"-",
"1.0",
",",
"topicConcentration",
"=",
"-",
"1.0",
",",
"seed",
"=",
"None",
",",
"checkpointInterval",
"=",
"10",
",",
"... | Train a LDA model.
:param rdd:
RDD of documents, which are tuples of document IDs and term
(word) count vectors. The term count vectors are "bags of
words" with a fixed-size vocabulary (where the vocabulary size
is the length of the vector). Document IDs must be unique
... | [
"Train",
"a",
"LDA",
"model",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/clustering.py#L999-L1039 |
19,080 | apache/spark | python/pyspark/mllib/common.py | _py2java | def _py2java(sc, obj):
""" Convert Python object into Java """
if isinstance(obj, RDD):
obj = _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)... | python | def _py2java(sc, obj):
""" Convert Python object into Java """
if isinstance(obj, RDD):
obj = _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)... | [
"def",
"_py2java",
"(",
"sc",
",",
"obj",
")",
":",
"if",
"isinstance",
"(",
"obj",
",",
"RDD",
")",
":",
"obj",
"=",
"_to_java_object_rdd",
"(",
"obj",
")",
"elif",
"isinstance",
"(",
"obj",
",",
"DataFrame",
")",
":",
"obj",
"=",
"obj",
".",
"_jd... | Convert Python object into Java | [
"Convert",
"Python",
"object",
"into",
"Java"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/common.py#L72-L89 |
19,081 | apache/spark | python/pyspark/mllib/common.py | callJavaFunc | def callJavaFunc(sc, func, *args):
""" Call Java Function """
args = [_py2java(sc, a) for a in args]
return _java2py(sc, func(*args)) | python | def callJavaFunc(sc, func, *args):
""" Call Java Function """
args = [_py2java(sc, a) for a in args]
return _java2py(sc, func(*args)) | [
"def",
"callJavaFunc",
"(",
"sc",
",",
"func",
",",
"*",
"args",
")",
":",
"args",
"=",
"[",
"_py2java",
"(",
"sc",
",",
"a",
")",
"for",
"a",
"in",
"args",
"]",
"return",
"_java2py",
"(",
"sc",
",",
"func",
"(",
"*",
"args",
")",
")"
] | Call Java Function | [
"Call",
"Java",
"Function"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/common.py#L120-L123 |
19,082 | apache/spark | python/pyspark/mllib/common.py | callMLlibFunc | def callMLlibFunc(name, *args):
""" Call API in PythonMLLibAPI """
sc = SparkContext.getOrCreate()
api = getattr(sc._jvm.PythonMLLibAPI(), name)
return callJavaFunc(sc, api, *args) | python | def callMLlibFunc(name, *args):
""" Call API in PythonMLLibAPI """
sc = SparkContext.getOrCreate()
api = getattr(sc._jvm.PythonMLLibAPI(), name)
return callJavaFunc(sc, api, *args) | [
"def",
"callMLlibFunc",
"(",
"name",
",",
"*",
"args",
")",
":",
"sc",
"=",
"SparkContext",
".",
"getOrCreate",
"(",
")",
"api",
"=",
"getattr",
"(",
"sc",
".",
"_jvm",
".",
"PythonMLLibAPI",
"(",
")",
",",
"name",
")",
"return",
"callJavaFunc",
"(",
... | Call API in PythonMLLibAPI | [
"Call",
"API",
"in",
"PythonMLLibAPI"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/common.py#L126-L130 |
19,083 | apache/spark | python/pyspark/mllib/common.py | inherit_doc | def inherit_doc(cls):
"""
A decorator that makes a class inherit documentation from its parents.
"""
for name, func in vars(cls).items():
# only inherit docstring for public functions
if name.startswith("_"):
continue
if not func.__doc__:
for parent in cls... | python | def inherit_doc(cls):
"""
A decorator that makes a class inherit documentation from its parents.
"""
for name, func in vars(cls).items():
# only inherit docstring for public functions
if name.startswith("_"):
continue
if not func.__doc__:
for parent in cls... | [
"def",
"inherit_doc",
"(",
"cls",
")",
":",
"for",
"name",
",",
"func",
"in",
"vars",
"(",
"cls",
")",
".",
"items",
"(",
")",
":",
"# only inherit docstring for public functions",
"if",
"name",
".",
"startswith",
"(",
"\"_\"",
")",
":",
"continue",
"if",
... | A decorator that makes a class inherit documentation from its parents. | [
"A",
"decorator",
"that",
"makes",
"a",
"class",
"inherit",
"documentation",
"from",
"its",
"parents",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/common.py#L149-L163 |
19,084 | apache/spark | python/pyspark/mllib/common.py | JavaModelWrapper.call | def call(self, name, *a):
"""Call method of java_model"""
return callJavaFunc(self._sc, getattr(self._java_model, name), *a) | python | def call(self, name, *a):
"""Call method of java_model"""
return callJavaFunc(self._sc, getattr(self._java_model, name), *a) | [
"def",
"call",
"(",
"self",
",",
"name",
",",
"*",
"a",
")",
":",
"return",
"callJavaFunc",
"(",
"self",
".",
"_sc",
",",
"getattr",
"(",
"self",
".",
"_java_model",
",",
"name",
")",
",",
"*",
"a",
")"
] | Call method of java_model | [
"Call",
"method",
"of",
"java_model"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/mllib/common.py#L144-L146 |
19,085 | apache/spark | python/pyspark/streaming/dstream.py | DStream.count | def count(self):
"""
Return a new DStream in which each RDD has a single element
generated by counting each RDD of this DStream.
"""
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).reduce(operator.add) | python | def count(self):
"""
Return a new DStream in which each RDD has a single element
generated by counting each RDD of this DStream.
"""
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).reduce(operator.add) | [
"def",
"count",
"(",
"self",
")",
":",
"return",
"self",
".",
"mapPartitions",
"(",
"lambda",
"i",
":",
"[",
"sum",
"(",
"1",
"for",
"_",
"in",
"i",
")",
"]",
")",
".",
"reduce",
"(",
"operator",
".",
"add",
")"
] | Return a new DStream in which each RDD has a single element
generated by counting each RDD of this DStream. | [
"Return",
"a",
"new",
"DStream",
"in",
"which",
"each",
"RDD",
"has",
"a",
"single",
"element",
"generated",
"by",
"counting",
"each",
"RDD",
"of",
"this",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L73-L78 |
19,086 | apache/spark | python/pyspark/streaming/dstream.py | DStream.filter | def filter(self, f):
"""
Return a new DStream containing only the elements that satisfy predicate.
"""
def func(iterator):
return filter(f, iterator)
return self.mapPartitions(func, True) | python | def filter(self, f):
"""
Return a new DStream containing only the elements that satisfy predicate.
"""
def func(iterator):
return filter(f, iterator)
return self.mapPartitions(func, True) | [
"def",
"filter",
"(",
"self",
",",
"f",
")",
":",
"def",
"func",
"(",
"iterator",
")",
":",
"return",
"filter",
"(",
"f",
",",
"iterator",
")",
"return",
"self",
".",
"mapPartitions",
"(",
"func",
",",
"True",
")"
] | Return a new DStream containing only the elements that satisfy predicate. | [
"Return",
"a",
"new",
"DStream",
"containing",
"only",
"the",
"elements",
"that",
"satisfy",
"predicate",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L80-L86 |
19,087 | apache/spark | python/pyspark/streaming/dstream.py | DStream.map | def map(self, f, preservesPartitioning=False):
"""
Return a new DStream by applying a function to each element of DStream.
"""
def func(iterator):
return map(f, iterator)
return self.mapPartitions(func, preservesPartitioning) | python | def map(self, f, preservesPartitioning=False):
"""
Return a new DStream by applying a function to each element of DStream.
"""
def func(iterator):
return map(f, iterator)
return self.mapPartitions(func, preservesPartitioning) | [
"def",
"map",
"(",
"self",
",",
"f",
",",
"preservesPartitioning",
"=",
"False",
")",
":",
"def",
"func",
"(",
"iterator",
")",
":",
"return",
"map",
"(",
"f",
",",
"iterator",
")",
"return",
"self",
".",
"mapPartitions",
"(",
"func",
",",
"preservesPa... | Return a new DStream by applying a function to each element of DStream. | [
"Return",
"a",
"new",
"DStream",
"by",
"applying",
"a",
"function",
"to",
"each",
"element",
"of",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L97-L103 |
19,088 | apache/spark | python/pyspark/streaming/dstream.py | DStream.reduce | def reduce(self, func):
"""
Return a new DStream in which each RDD has a single element
generated by reducing each RDD of this DStream.
"""
return self.map(lambda x: (None, x)).reduceByKey(func, 1).map(lambda x: x[1]) | python | def reduce(self, func):
"""
Return a new DStream in which each RDD has a single element
generated by reducing each RDD of this DStream.
"""
return self.map(lambda x: (None, x)).reduceByKey(func, 1).map(lambda x: x[1]) | [
"def",
"reduce",
"(",
"self",
",",
"func",
")",
":",
"return",
"self",
".",
"map",
"(",
"lambda",
"x",
":",
"(",
"None",
",",
"x",
")",
")",
".",
"reduceByKey",
"(",
"func",
",",
"1",
")",
".",
"map",
"(",
"lambda",
"x",
":",
"x",
"[",
"1",
... | Return a new DStream in which each RDD has a single element
generated by reducing each RDD of this DStream. | [
"Return",
"a",
"new",
"DStream",
"in",
"which",
"each",
"RDD",
"has",
"a",
"single",
"element",
"generated",
"by",
"reducing",
"each",
"RDD",
"of",
"this",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L121-L126 |
19,089 | apache/spark | python/pyspark/streaming/dstream.py | DStream.reduceByKey | def reduceByKey(self, func, numPartitions=None):
"""
Return a new DStream by applying reduceByKey to each RDD.
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
return self.combineByKey(lambda x: x, func, func, numPartitions) | python | def reduceByKey(self, func, numPartitions=None):
"""
Return a new DStream by applying reduceByKey to each RDD.
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
return self.combineByKey(lambda x: x, func, func, numPartitions) | [
"def",
"reduceByKey",
"(",
"self",
",",
"func",
",",
"numPartitions",
"=",
"None",
")",
":",
"if",
"numPartitions",
"is",
"None",
":",
"numPartitions",
"=",
"self",
".",
"_sc",
".",
"defaultParallelism",
"return",
"self",
".",
"combineByKey",
"(",
"lambda",
... | Return a new DStream by applying reduceByKey to each RDD. | [
"Return",
"a",
"new",
"DStream",
"by",
"applying",
"reduceByKey",
"to",
"each",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L128-L134 |
19,090 | apache/spark | python/pyspark/streaming/dstream.py | DStream.combineByKey | def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
numPartitions=None):
"""
Return a new DStream by applying combineByKey to each RDD.
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
def func(rdd):
... | python | def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
numPartitions=None):
"""
Return a new DStream by applying combineByKey to each RDD.
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
def func(rdd):
... | [
"def",
"combineByKey",
"(",
"self",
",",
"createCombiner",
",",
"mergeValue",
",",
"mergeCombiners",
",",
"numPartitions",
"=",
"None",
")",
":",
"if",
"numPartitions",
"is",
"None",
":",
"numPartitions",
"=",
"self",
".",
"_sc",
".",
"defaultParallelism",
"de... | Return a new DStream by applying combineByKey to each RDD. | [
"Return",
"a",
"new",
"DStream",
"by",
"applying",
"combineByKey",
"to",
"each",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L136-L146 |
19,091 | apache/spark | python/pyspark/streaming/dstream.py | DStream.partitionBy | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the DStream in which each RDD are partitioned
using the specified partitioner.
"""
return self.transform(lambda rdd: rdd.partitionBy(numPartitions, partitionFunc)) | python | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the DStream in which each RDD are partitioned
using the specified partitioner.
"""
return self.transform(lambda rdd: rdd.partitionBy(numPartitions, partitionFunc)) | [
"def",
"partitionBy",
"(",
"self",
",",
"numPartitions",
",",
"partitionFunc",
"=",
"portable_hash",
")",
":",
"return",
"self",
".",
"transform",
"(",
"lambda",
"rdd",
":",
"rdd",
".",
"partitionBy",
"(",
"numPartitions",
",",
"partitionFunc",
")",
")"
] | Return a copy of the DStream in which each RDD are partitioned
using the specified partitioner. | [
"Return",
"a",
"copy",
"of",
"the",
"DStream",
"in",
"which",
"each",
"RDD",
"are",
"partitioned",
"using",
"the",
"specified",
"partitioner",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L148-L153 |
19,092 | apache/spark | python/pyspark/streaming/dstream.py | DStream.foreachRDD | def foreachRDD(self, func):
"""
Apply a function to each RDD in this DStream.
"""
if func.__code__.co_argcount == 1:
old_func = func
func = lambda t, rdd: old_func(rdd)
jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer)
api = self._... | python | def foreachRDD(self, func):
"""
Apply a function to each RDD in this DStream.
"""
if func.__code__.co_argcount == 1:
old_func = func
func = lambda t, rdd: old_func(rdd)
jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer)
api = self._... | [
"def",
"foreachRDD",
"(",
"self",
",",
"func",
")",
":",
"if",
"func",
".",
"__code__",
".",
"co_argcount",
"==",
"1",
":",
"old_func",
"=",
"func",
"func",
"=",
"lambda",
"t",
",",
"rdd",
":",
"old_func",
"(",
"rdd",
")",
"jfunc",
"=",
"TransformFun... | Apply a function to each RDD in this DStream. | [
"Apply",
"a",
"function",
"to",
"each",
"RDD",
"in",
"this",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L155-L164 |
19,093 | apache/spark | python/pyspark/streaming/dstream.py | DStream.pprint | def pprint(self, num=10):
"""
Print the first num elements of each RDD generated in this DStream.
@param num: the number of elements from the first will be printed.
"""
def takeAndPrint(time, rdd):
taken = rdd.take(num + 1)
print("------------------------... | python | def pprint(self, num=10):
"""
Print the first num elements of each RDD generated in this DStream.
@param num: the number of elements from the first will be printed.
"""
def takeAndPrint(time, rdd):
taken = rdd.take(num + 1)
print("------------------------... | [
"def",
"pprint",
"(",
"self",
",",
"num",
"=",
"10",
")",
":",
"def",
"takeAndPrint",
"(",
"time",
",",
"rdd",
")",
":",
"taken",
"=",
"rdd",
".",
"take",
"(",
"num",
"+",
"1",
")",
"print",
"(",
"\"-------------------------------------------\"",
")",
... | Print the first num elements of each RDD generated in this DStream.
@param num: the number of elements from the first will be printed. | [
"Print",
"the",
"first",
"num",
"elements",
"of",
"each",
"RDD",
"generated",
"in",
"this",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L166-L183 |
19,094 | apache/spark | python/pyspark/streaming/dstream.py | DStream.persist | def persist(self, storageLevel):
"""
Persist the RDDs of this DStream with the given storage level
"""
self.is_cached = True
javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
self._jdstream.persist(javaStorageLevel)
return self | python | def persist(self, storageLevel):
"""
Persist the RDDs of this DStream with the given storage level
"""
self.is_cached = True
javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
self._jdstream.persist(javaStorageLevel)
return self | [
"def",
"persist",
"(",
"self",
",",
"storageLevel",
")",
":",
"self",
".",
"is_cached",
"=",
"True",
"javaStorageLevel",
"=",
"self",
".",
"_sc",
".",
"_getJavaStorageLevel",
"(",
"storageLevel",
")",
"self",
".",
"_jdstream",
".",
"persist",
"(",
"javaStora... | Persist the RDDs of this DStream with the given storage level | [
"Persist",
"the",
"RDDs",
"of",
"this",
"DStream",
"with",
"the",
"given",
"storage",
"level"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L219-L226 |
19,095 | apache/spark | python/pyspark/streaming/dstream.py | DStream.checkpoint | def checkpoint(self, interval):
"""
Enable periodic checkpointing of RDDs of this DStream
@param interval: time in seconds, after each period of that, generated
RDD will be checkpointed
"""
self.is_checkpointed = True
self._jdstream.checkpoint(se... | python | def checkpoint(self, interval):
"""
Enable periodic checkpointing of RDDs of this DStream
@param interval: time in seconds, after each period of that, generated
RDD will be checkpointed
"""
self.is_checkpointed = True
self._jdstream.checkpoint(se... | [
"def",
"checkpoint",
"(",
"self",
",",
"interval",
")",
":",
"self",
".",
"is_checkpointed",
"=",
"True",
"self",
".",
"_jdstream",
".",
"checkpoint",
"(",
"self",
".",
"_ssc",
".",
"_jduration",
"(",
"interval",
")",
")",
"return",
"self"
] | Enable periodic checkpointing of RDDs of this DStream
@param interval: time in seconds, after each period of that, generated
RDD will be checkpointed | [
"Enable",
"periodic",
"checkpointing",
"of",
"RDDs",
"of",
"this",
"DStream"
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L228-L237 |
19,096 | apache/spark | python/pyspark/streaming/dstream.py | DStream.groupByKey | def groupByKey(self, numPartitions=None):
"""
Return a new DStream by applying groupByKey on each RDD.
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
return self.transform(lambda rdd: rdd.groupByKey(numPartitions)) | python | def groupByKey(self, numPartitions=None):
"""
Return a new DStream by applying groupByKey on each RDD.
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
return self.transform(lambda rdd: rdd.groupByKey(numPartitions)) | [
"def",
"groupByKey",
"(",
"self",
",",
"numPartitions",
"=",
"None",
")",
":",
"if",
"numPartitions",
"is",
"None",
":",
"numPartitions",
"=",
"self",
".",
"_sc",
".",
"defaultParallelism",
"return",
"self",
".",
"transform",
"(",
"lambda",
"rdd",
":",
"rd... | Return a new DStream by applying groupByKey on each RDD. | [
"Return",
"a",
"new",
"DStream",
"by",
"applying",
"groupByKey",
"on",
"each",
"RDD",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L239-L245 |
19,097 | apache/spark | python/pyspark/streaming/dstream.py | DStream.countByValue | def countByValue(self):
"""
Return a new DStream in which each RDD contains the counts of each
distinct value in each RDD of this DStream.
"""
return self.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x+y) | python | def countByValue(self):
"""
Return a new DStream in which each RDD contains the counts of each
distinct value in each RDD of this DStream.
"""
return self.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x+y) | [
"def",
"countByValue",
"(",
"self",
")",
":",
"return",
"self",
".",
"map",
"(",
"lambda",
"x",
":",
"(",
"x",
",",
"1",
")",
")",
".",
"reduceByKey",
"(",
"lambda",
"x",
",",
"y",
":",
"x",
"+",
"y",
")"
] | Return a new DStream in which each RDD contains the counts of each
distinct value in each RDD of this DStream. | [
"Return",
"a",
"new",
"DStream",
"in",
"which",
"each",
"RDD",
"contains",
"the",
"counts",
"of",
"each",
"distinct",
"value",
"in",
"each",
"RDD",
"of",
"this",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L247-L252 |
19,098 | apache/spark | python/pyspark/streaming/dstream.py | DStream.saveAsTextFiles | def saveAsTextFiles(self, prefix, suffix=None):
"""
Save each RDD in this DStream as at text file, using string
representation of elements.
"""
def saveAsTextFile(t, rdd):
path = rddToFileName(prefix, suffix, t)
try:
rdd.saveAsTextFile(path... | python | def saveAsTextFiles(self, prefix, suffix=None):
"""
Save each RDD in this DStream as at text file, using string
representation of elements.
"""
def saveAsTextFile(t, rdd):
path = rddToFileName(prefix, suffix, t)
try:
rdd.saveAsTextFile(path... | [
"def",
"saveAsTextFiles",
"(",
"self",
",",
"prefix",
",",
"suffix",
"=",
"None",
")",
":",
"def",
"saveAsTextFile",
"(",
"t",
",",
"rdd",
")",
":",
"path",
"=",
"rddToFileName",
"(",
"prefix",
",",
"suffix",
",",
"t",
")",
"try",
":",
"rdd",
".",
... | Save each RDD in this DStream as at text file, using string
representation of elements. | [
"Save",
"each",
"RDD",
"in",
"this",
"DStream",
"as",
"at",
"text",
"file",
"using",
"string",
"representation",
"of",
"elements",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L254-L268 |
19,099 | apache/spark | python/pyspark/streaming/dstream.py | DStream.transform | def transform(self, func):
"""
Return a new DStream in which each RDD is generated by applying a function
on each RDD of this DStream.
`func` can have one argument of `rdd`, or have two arguments of
(`time`, `rdd`)
"""
if func.__code__.co_argcount == 1:
... | python | def transform(self, func):
"""
Return a new DStream in which each RDD is generated by applying a function
on each RDD of this DStream.
`func` can have one argument of `rdd`, or have two arguments of
(`time`, `rdd`)
"""
if func.__code__.co_argcount == 1:
... | [
"def",
"transform",
"(",
"self",
",",
"func",
")",
":",
"if",
"func",
".",
"__code__",
".",
"co_argcount",
"==",
"1",
":",
"oldfunc",
"=",
"func",
"func",
"=",
"lambda",
"t",
",",
"rdd",
":",
"oldfunc",
"(",
"rdd",
")",
"assert",
"func",
".",
"__co... | Return a new DStream in which each RDD is generated by applying a function
on each RDD of this DStream.
`func` can have one argument of `rdd`, or have two arguments of
(`time`, `rdd`) | [
"Return",
"a",
"new",
"DStream",
"in",
"which",
"each",
"RDD",
"is",
"generated",
"by",
"applying",
"a",
"function",
"on",
"each",
"RDD",
"of",
"this",
"DStream",
"."
] | 618d6bff71073c8c93501ab7392c3cc579730f0b | https://github.com/apache/spark/blob/618d6bff71073c8c93501ab7392c3cc579730f0b/python/pyspark/streaming/dstream.py#L287-L299 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.