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Bloom Filter (#8615)
* Bloom filter with tests * has functions constant * fix type * isort * passing ruff * type hints * type hints * from fail to erro * captital leter * type hints requested by boot * descriptive name for m * more descriptibe arguments II * moved movies_test to doctest * commented doctest * removed test_probability * estimated error * added types * again hash_ * Update data_structures/hashing/bloom_filter.py Co-authored-by: Christian Clauss <cclauss@me.com> * from b to bloom * Update data_structures/hashing/bloom_filter.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update data_structures/hashing/bloom_filter.py Co-authored-by: Christian Clauss <cclauss@me.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * syntax error in dict comprehension * from goodfather to godfather * removed Interestellar * forgot the last Godfather * Revert "removed Interestellar" This reverts commit 35fa5f5c4bf101d073aad43c37b0a423d8975071. * pretty dict * Apply suggestions from code review * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update bloom_filter.py --------- Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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data_structures/hashing/bloom_filter.py
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data_structures/hashing/bloom_filter.py
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"""
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See https://en.wikipedia.org/wiki/Bloom_filter
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The use of this data structure is to test membership in a set.
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Compared to Python's built-in set() it is more space-efficient.
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In the following example, only 8 bits of memory will be used:
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>>> bloom = Bloom(size=8)
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Initially, the filter contains all zeros:
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>>> bloom.bitstring
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'00000000'
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When an element is added, two bits are set to 1
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since there are 2 hash functions in this implementation:
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>>> "Titanic" in bloom
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False
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>>> bloom.add("Titanic")
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>>> bloom.bitstring
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'01100000'
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>>> "Titanic" in bloom
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True
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However, sometimes only one bit is added
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because both hash functions return the same value
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>>> bloom.add("Avatar")
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>>> "Avatar" in bloom
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True
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>>> bloom.format_hash("Avatar")
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'00000100'
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>>> bloom.bitstring
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'01100100'
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Not added elements should return False ...
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>>> not_present_films = ("The Godfather", "Interstellar", "Parasite", "Pulp Fiction")
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>>> {
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... film: bloom.format_hash(film) for film in not_present_films
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... } # doctest: +NORMALIZE_WHITESPACE
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{'The Godfather': '00000101',
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'Interstellar': '00000011',
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'Parasite': '00010010',
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'Pulp Fiction': '10000100'}
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>>> any(film in bloom for film in not_present_films)
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False
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but sometimes there are false positives:
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>>> "Ratatouille" in bloom
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True
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>>> bloom.format_hash("Ratatouille")
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'01100000'
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The probability increases with the number of elements added.
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The probability decreases with the number of bits in the bitarray.
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>>> bloom.estimated_error_rate
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0.140625
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>>> bloom.add("The Godfather")
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>>> bloom.estimated_error_rate
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0.25
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>>> bloom.bitstring
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'01100101'
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"""
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from hashlib import md5, sha256
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HASH_FUNCTIONS = (sha256, md5)
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class Bloom:
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def __init__(self, size: int = 8) -> None:
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self.bitarray = 0b0
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self.size = size
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def add(self, value: str) -> None:
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h = self.hash_(value)
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self.bitarray |= h
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def exists(self, value: str) -> bool:
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h = self.hash_(value)
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return (h & self.bitarray) == h
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def __contains__(self, other: str) -> bool:
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return self.exists(other)
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def format_bin(self, bitarray: int) -> str:
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res = bin(bitarray)[2:]
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return res.zfill(self.size)
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@property
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def bitstring(self) -> str:
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return self.format_bin(self.bitarray)
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def hash_(self, value: str) -> int:
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res = 0b0
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for func in HASH_FUNCTIONS:
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position = (
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int.from_bytes(func(value.encode()).digest(), "little") % self.size
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)
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res |= 2**position
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return res
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def format_hash(self, value: str) -> str:
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return self.format_bin(self.hash_(value))
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@property
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def estimated_error_rate(self) -> float:
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n_ones = bin(self.bitarray).count("1")
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return (n_ones / self.size) ** len(HASH_FUNCTIONS)
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