260 lines
9.4 KiB
Python
260 lines
9.4 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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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"""A key-value[] store that implements reservoir sampling on the values."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import random
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import threading
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class Reservoir(object):
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"""A map-to-arrays container, with deterministic Reservoir Sampling.
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Items are added with an associated key. Items may be retrieved by key, and
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a list of keys can also be retrieved. If size is not zero, then it dictates
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the maximum number of items that will be stored with each key. Once there are
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more items for a given key, they are replaced via reservoir sampling, such
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that each item has an equal probability of being included in the sample.
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Deterministic means that for any given seed and bucket size, the sequence of
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values that are kept for any given tag will always be the same, and that this
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is independent of any insertions on other tags. That is:
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>>> separate_reservoir = reservoir.Reservoir(10)
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>>> interleaved_reservoir = reservoir.Reservoir(10)
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>>> for i in xrange(100):
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>>> separate_reservoir.AddItem('key1', i)
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>>> for i in xrange(100):
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>>> separate_reservoir.AddItem('key2', i)
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>>> for i in xrange(100):
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>>> interleaved_reservoir.AddItem('key1', i)
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>>> interleaved_reservoir.AddItem('key2', i)
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separate_reservoir and interleaved_reservoir will be in identical states.
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See: https://en.wikipedia.org/wiki/Reservoir_sampling
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Adding items has amortized O(1) runtime.
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Fields:
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always_keep_last: Whether the latest seen sample is always at the
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end of the reservoir. Defaults to True.
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size: An integer of the maximum number of samples.
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"""
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def __init__(self, size, seed=0, always_keep_last=True):
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"""Creates a new reservoir.
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Args:
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size: The number of values to keep in the reservoir for each tag. If 0,
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all values will be kept.
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seed: The seed of the random number generator to use when sampling.
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Different values for |seed| will produce different samples from the same
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input items.
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always_keep_last: Whether to always keep the latest seen item in the
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end of the reservoir. Defaults to True.
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Raises:
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ValueError: If size is negative or not an integer.
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"""
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if size < 0 or size != round(size):
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raise ValueError('size must be nonegative integer, was %s' % size)
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self._buckets = collections.defaultdict(
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lambda: _ReservoirBucket(size, random.Random(seed), always_keep_last))
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# _mutex guards the keys - creating new keys, retrieving by key, etc
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# the internal items are guarded by the ReservoirBuckets' internal mutexes
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self._mutex = threading.Lock()
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self.size = size
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self.always_keep_last = always_keep_last
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def Keys(self):
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"""Return all the keys in the reservoir.
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Returns:
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['list', 'of', 'keys'] in the Reservoir.
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"""
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with self._mutex:
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return list(self._buckets.keys())
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def Items(self, key):
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"""Return items associated with given key.
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Args:
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key: The key for which we are finding associated items.
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Raises:
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KeyError: If the key is not found in the reservoir.
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Returns:
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[list, of, items] associated with that key.
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"""
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with self._mutex:
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if key not in self._buckets:
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raise KeyError('Key %s was not found in Reservoir' % key)
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bucket = self._buckets[key]
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return bucket.Items()
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def AddItem(self, key, item, f=lambda x: x):
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"""Add a new item to the Reservoir with the given tag.
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If the reservoir has not yet reached full size, the new item is guaranteed
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to be added. If the reservoir is full, then behavior depends on the
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always_keep_last boolean.
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If always_keep_last was set to true, the new item is guaranteed to be added
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to the reservoir, and either the previous last item will be replaced, or
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(with low probability) an older item will be replaced.
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If always_keep_last was set to false, then the new item will replace an
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old item with low probability.
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If f is provided, it will be applied to transform item (lazily, iff item is
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going to be included in the reservoir).
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Args:
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key: The key to store the item under.
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item: The item to add to the reservoir.
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f: An optional function to transform the item prior to addition.
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"""
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with self._mutex:
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bucket = self._buckets[key]
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bucket.AddItem(item, f)
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def FilterItems(self, filterFn, key=None):
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"""Filter items within a Reservoir, using a filtering function.
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Args:
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filterFn: A function that returns True for the items to be kept.
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key: An optional bucket key to filter. If not specified, will filter all
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all buckets.
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Returns:
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The number of items removed.
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"""
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with self._mutex:
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if key:
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if key in self._buckets:
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return self._buckets[key].FilterItems(filterFn)
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else:
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return 0
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else:
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return sum(bucket.FilterItems(filterFn)
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for bucket in self._buckets.values())
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class _ReservoirBucket(object):
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"""A container for items from a stream, that implements reservoir sampling.
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It always stores the most recent item as its final item.
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"""
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def __init__(self, _max_size, _random=None, always_keep_last=True):
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"""Create the _ReservoirBucket.
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Args:
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_max_size: The maximum size the reservoir bucket may grow to. If size is
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zero, the bucket has unbounded size.
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_random: The random number generator to use. If not specified, defaults to
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random.Random(0).
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always_keep_last: Whether the latest seen item should always be included
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in the end of the bucket.
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Raises:
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ValueError: if the size is not a nonnegative integer.
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"""
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if _max_size < 0 or _max_size != round(_max_size):
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raise ValueError('_max_size must be nonegative int, was %s' % _max_size)
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self.items = []
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# This mutex protects the internal items, ensuring that calls to Items and
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# AddItem are thread-safe
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self._mutex = threading.Lock()
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self._max_size = _max_size
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self._num_items_seen = 0
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if _random is not None:
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self._random = _random
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else:
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self._random = random.Random(0)
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self.always_keep_last = always_keep_last
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def AddItem(self, item, f=lambda x: x):
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"""Add an item to the ReservoirBucket, replacing an old item if necessary.
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The new item is guaranteed to be added to the bucket, and to be the last
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element in the bucket. If the bucket has reached capacity, then an old item
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will be replaced. With probability (_max_size/_num_items_seen) a random item
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in the bucket will be popped out and the new item will be appended
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to the end. With probability (1 - _max_size/_num_items_seen)
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the last item in the bucket will be replaced.
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Since the O(n) replacements occur with O(1/_num_items_seen) likelihood,
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the amortized runtime is O(1).
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Args:
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item: The item to add to the bucket.
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f: A function to transform item before addition, if it will be kept in
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the reservoir.
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"""
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with self._mutex:
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if len(self.items) < self._max_size or self._max_size == 0:
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self.items.append(f(item))
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else:
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r = self._random.randint(0, self._num_items_seen)
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if r < self._max_size:
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self.items.pop(r)
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self.items.append(f(item))
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elif self.always_keep_last:
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self.items[-1] = f(item)
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self._num_items_seen += 1
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def FilterItems(self, filterFn):
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"""Filter items in a ReservoirBucket, using a filtering function.
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Filtering items from the reservoir bucket must update the
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internal state variable self._num_items_seen, which is used for determining
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the rate of replacement in reservoir sampling. Ideally, self._num_items_seen
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would contain the exact number of items that have ever seen by the
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ReservoirBucket and satisfy filterFn. However, the ReservoirBucket does not
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have access to all items seen -- it only has access to the subset of items
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that have survived sampling (self.items). Therefore, we estimate
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self._num_items_seen by scaling it by the same ratio as the ratio of items
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not removed from self.items.
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Args:
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filterFn: A function that returns True for items to be kept.
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Returns:
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The number of items removed from the bucket.
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"""
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with self._mutex:
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size_before = len(self.items)
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self.items = list(filter(filterFn, self.items))
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size_diff = size_before - len(self.items)
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# Estimate a correction the number of items seen
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prop_remaining = len(self.items) / float(
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size_before) if size_before > 0 else 0
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self._num_items_seen = int(round(self._num_items_seen * prop_remaining))
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return size_diff
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def Items(self):
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"""Get all the items in the bucket."""
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with self._mutex:
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return list(self.items)
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