3cca196220
The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1. ~~~ In [14]: import random In [15]: r1 = random.Random(10) In [16]: r1.randint(0, 1) Out[16]: 1 In [17]: r1.random() Out[17]: 0.4288890546751146 In [18]: r1.random() Out[18]: 0.5780913011344704 In [19]: r2 = random.Random(10) In [20]: r2.randint(0, 1) Out[20]: 1 In [21]: r2.randint(0, 1) Out[21]: 0 In [22]: r2.random() Out[22]: 0.5780913011344704 ~~~ Note: The new tests are not for this bug fix. Author: Xiangrui Meng <meng@databricks.com> Closes #3010 from mengxr/SPARK-4148 and squashes the following commits: 869ae4b [Xiangrui Meng] move tests tests.py c1bacd9 [Xiangrui Meng] fix seed distribution and add some tests for rdd.sample
137 lines
4.8 KiB
Python
137 lines
4.8 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import random
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class RDDSamplerBase(object):
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def __init__(self, withReplacement, seed=None):
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try:
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import numpy
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self._use_numpy = True
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except ImportError:
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print >> sys.stderr, (
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"NumPy does not appear to be installed. "
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"Falling back to default random generator for sampling.")
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self._use_numpy = False
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self._seed = seed if seed is not None else random.randint(0, 2 ** 32 - 1)
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self._withReplacement = withReplacement
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self._random = None
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self._split = None
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self._rand_initialized = False
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def initRandomGenerator(self, split):
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if self._use_numpy:
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import numpy
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self._random = numpy.random.RandomState(self._seed ^ split)
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else:
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self._random = random.Random(self._seed ^ split)
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# mixing because the initial seeds are close to each other
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for _ in xrange(10):
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self._random.randint(0, 1)
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self._split = split
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self._rand_initialized = True
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def getUniformSample(self, split):
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if not self._rand_initialized or split != self._split:
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self.initRandomGenerator(split)
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if self._use_numpy:
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return self._random.random_sample()
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else:
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return self._random.uniform(0.0, 1.0)
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def getPoissonSample(self, split, mean):
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if not self._rand_initialized or split != self._split:
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self.initRandomGenerator(split)
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if self._use_numpy:
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return self._random.poisson(mean)
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else:
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# here we simulate drawing numbers n_i ~ Poisson(lambda = 1/mean) by
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# drawing a sequence of numbers delta_j ~ Exp(mean)
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num_arrivals = 1
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cur_time = 0.0
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cur_time += self._random.expovariate(mean)
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if cur_time > 1.0:
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return 0
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while(cur_time <= 1.0):
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cur_time += self._random.expovariate(mean)
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num_arrivals += 1
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return (num_arrivals - 1)
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def shuffle(self, vals):
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if self._random is None:
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self.initRandomGenerator(0) # this should only ever called on the master so
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# the split does not matter
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if self._use_numpy:
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self._random.shuffle(vals)
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else:
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self._random.shuffle(vals, self._random.random)
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class RDDSampler(RDDSamplerBase):
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def __init__(self, withReplacement, fraction, seed=None):
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RDDSamplerBase.__init__(self, withReplacement, seed)
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self._fraction = fraction
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def func(self, split, iterator):
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if self._withReplacement:
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for obj in iterator:
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# For large datasets, the expected number of occurrences of each element in
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# a sample with replacement is Poisson(frac). We use that to get a count for
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# each element.
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count = self.getPoissonSample(split, mean=self._fraction)
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for _ in range(0, count):
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yield obj
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else:
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for obj in iterator:
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if self.getUniformSample(split) <= self._fraction:
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yield obj
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class RDDStratifiedSampler(RDDSamplerBase):
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def __init__(self, withReplacement, fractions, seed=None):
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RDDSamplerBase.__init__(self, withReplacement, seed)
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self._fractions = fractions
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def func(self, split, iterator):
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if self._withReplacement:
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for key, val in iterator:
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# For large datasets, the expected number of occurrences of each element in
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# a sample with replacement is Poisson(frac). We use that to get a count for
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# each element.
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count = self.getPoissonSample(split, mean=self._fractions[key])
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for _ in range(0, count):
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yield key, val
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else:
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for key, val in iterator:
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if self.getUniformSample(split) <= self._fractions[key]:
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yield key, val
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