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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Python package for random data generation.
"""
from pyspark.rdd import RDD
from pyspark.mllib._common import _deserialize_double, _deserialize_double_vector
from pyspark.serializers import NoOpSerializer
class RandomRDDs:
"""
Generator methods for creating RDDs comprised of i.i.d samples from
some distribution.
"""
@staticmethod
def uniformRDD(sc, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d. samples from the
uniform distribution on [0.0, 1.0].
To transform the distribution in the generated RDD from U[0.0, 1.0]
to U[a, b], use
C{RandomRDDs.uniformRDD(sc, n, p, seed)\
.map(lambda v: a + (b - a) * v)}
>>> x = RandomRDDs.uniformRDD(sc, 100).collect()
>>> len(x)
100
>>> max(x) <= 1.0 and min(x) >= 0.0
True
>>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()
4
>>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()
>>> parts == sc.defaultParallelism
True
"""
jrdd = sc._jvm.PythonMLLibAPI().uniformRDD(sc._jsc, size, numPartitions, seed)
uniform = RDD(jrdd, sc, NoOpSerializer())
return uniform.map(lambda bytes: _deserialize_double(bytearray(bytes)))
@staticmethod
def normalRDD(sc, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d samples from the standard normal
distribution.
To transform the distribution in the generated RDD from standard normal
to some other normal N(mean, sigma), use
C{RandomRDDs.normal(sc, n, p, seed)\
.map(lambda v: mean + sigma * v)}
>>> x = RandomRDDs.normalRDD(sc, 1000, seed=1L)
>>> stats = x.stats()
>>> stats.count()
1000L
>>> abs(stats.mean() - 0.0) < 0.1
True
>>> abs(stats.stdev() - 1.0) < 0.1
True
"""
jrdd = sc._jvm.PythonMLLibAPI().normalRDD(sc._jsc, size, numPartitions, seed)
normal = RDD(jrdd, sc, NoOpSerializer())
return normal.map(lambda bytes: _deserialize_double(bytearray(bytes)))
@staticmethod
def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
"""
Generates an RDD comprised of i.i.d samples from the Poisson
distribution with the input mean.
>>> mean = 100.0
>>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=1L)
>>> stats = x.stats()
>>> stats.count()
1000L
>>> abs(stats.mean() - mean) < 0.5
True
>>> from math import sqrt
>>> abs(stats.stdev() - sqrt(mean)) < 0.5
True
"""
jrdd = sc._jvm.PythonMLLibAPI().poissonRDD(sc._jsc, mean, size, numPartitions, seed)
poisson = RDD(jrdd, sc, NoOpSerializer())
return poisson.map(lambda bytes: _deserialize_double(bytearray(bytes)))
@staticmethod
def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d samples drawn
from the uniform distribution on [0.0 1.0].
>>> import numpy as np
>>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
>>> mat.shape
(10, 10)
>>> mat.max() <= 1.0 and mat.min() >= 0.0
True
>>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
4
"""
jrdd = sc._jvm.PythonMLLibAPI() \
.uniformVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
uniform = RDD(jrdd, sc, NoOpSerializer())
return uniform.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))
@staticmethod
def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d samples drawn
from the standard normal distribution.
>>> import numpy as np
>>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1L).collect())
>>> mat.shape
(100, 100)
>>> abs(mat.mean() - 0.0) < 0.1
True
>>> abs(mat.std() - 1.0) < 0.1
True
"""
jrdd = sc._jvm.PythonMLLibAPI() \
.normalVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
normal = RDD(jrdd, sc, NoOpSerializer())
return normal.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))
@staticmethod
def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
"""
Generates an RDD comprised of vectors containing i.i.d samples drawn
from the Poisson distribution with the input mean.
>>> import numpy as np
>>> mean = 100.0
>>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1L)
>>> mat = np.mat(rdd.collect())
>>> mat.shape
(100, 100)
>>> abs(mat.mean() - mean) < 0.5
True
>>> from math import sqrt
>>> abs(mat.std() - sqrt(mean)) < 0.5
True
"""
jrdd = sc._jvm.PythonMLLibAPI() \
.poissonVectorRDD(sc._jsc, mean, numRows, numCols, numPartitions, seed)
poisson = RDD(jrdd, sc, NoOpSerializer())
return poisson.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))
def _test():
import doctest
from pyspark.context import SparkContext
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
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