113 lines
4 KiB
Python
113 lines
4 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 RDDSampler(object):
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def __init__(self, withReplacement, fraction, seed):
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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, "NumPy does not appear to be installed. Falling back to default random generator for sampling."
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self._use_numpy = False
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self._seed = seed
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self._withReplacement = withReplacement
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self._fraction = fraction
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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)
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for _ in range(0, split):
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# discard the next few values in the sequence to have a
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# different seed for the different splits
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self._random.randint(sys.maxint)
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else:
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import random
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random.seed(self._seed)
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for _ in range(0, split):
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# discard the next few values in the sequence to have a
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# different seed for the different splits
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random.randint(0, sys.maxint)
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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 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 += 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 += 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 == None or split != self._split:
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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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random.shuffle(vals, self._random)
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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 a sample with
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# replacement is Poisson(frac). We use that to get a count for 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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