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sequence_based.py
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sequence_based.py
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# built-in
from difflib import SequenceMatcher as _SequenceMatcher
# app
from .base import BaseSimilarity as _BaseSimilarity
from ..utils import find_ngrams
try:
import numpy
except ImportError:
from array import array
numpy = None
__all__ = [
'lcsseq', 'lcsstr', 'ratcliff_obershelp',
'LCSSeq', 'LCSStr', 'RatcliffObershelp',
]
class LCSSeq(_BaseSimilarity):
"""longest common subsequence similarity
https://en.wikipedia.org/wiki/Longest_common_subsequence_problem
"""
def __init__(self, qval=1, test_func=None):
self.qval = qval
self.test_func = test_func or self._ident
def _dynamic(self, seq1, seq2):
"""
https://github.com/chrislit/abydos/blob/master/abydos/distance/_lcsseq.py
http://www.dis.uniroma1.it/~bonifaci/algo/LCSSEQ.py
http://rosettacode.org/wiki/Longest_common_subsequence#Dynamic_Programming_8
"""
if numpy:
lengths = numpy.zeros((len(seq1) + 1, len(seq2) + 1), dtype=numpy.int)
else:
lengths = [array('L', [0] * (len(seq2) + 1)) for _ in range(len(seq1) + 1)]
# row 0 and column 0 are initialized to 0 already
for i, char1 in enumerate(seq1):
for j, char2 in enumerate(seq2):
if char1 == char2:
lengths[i + 1][j + 1] = lengths[i][j] + 1
else:
lengths[i + 1][j + 1] = max(lengths[i + 1][j], lengths[i][j + 1])
# read the substring out from the matrix
result = ''
i, j = len(seq1), len(seq2)
while i != 0 and j != 0:
if lengths[i][j] == lengths[i - 1][j]:
i -= 1
elif lengths[i][j] == lengths[i][j - 1]:
j -= 1
else:
assert seq1[i - 1] == seq2[j - 1]
result = seq1[i - 1] + result
i -= 1
j -= 1
return result
def _recursive(self, *sequences):
if not all(sequences):
return type(sequences[0])() # empty sequence
if self.test_func(*[s[-1] for s in sequences]):
c = sequences[0][-1]
sequences = [s[:-1] for s in sequences]
return self(*sequences) + c
m = type(sequences[0])() # empty sequence
for i, s in enumerate(sequences):
ss = sequences[:i] + (s[:-1], ) + sequences[i + 1:]
m = max([self(*ss), m], key=len)
return m
def __call__(self, *sequences):
if not sequences:
return ''
sequences = self._get_sequences(*sequences)
if len(sequences) == 2:
return self._dynamic(*sequences)
else:
return self._recursive(*sequences)
def similarity(self, *sequences):
return len(self(*sequences))
class LCSStr(_BaseSimilarity):
"""longest common substring similarity
"""
def _standart(self, s1, s2):
matcher = _SequenceMatcher(a=s1, b=s2)
match = matcher.find_longest_match(0, len(s1), 0, len(s2))
return s1[match.a: match.a + match.size]
def _custom(self, *sequences):
short = min(sequences, key=len)
length = len(short)
for n in range(length, 0, -1):
for subseq in find_ngrams(short, n):
subseq = ''.join(subseq)
for seq in sequences:
if subseq not in seq:
break
else:
return subseq
return type(short)() # empty sequence
def __call__(self, *sequences):
if not all(sequences):
return ''
length = len(sequences)
if length == 0:
return ''
if length == 1:
return sequences[0]
sequences = self._get_sequences(*sequences)
if length == 2 and max(map(len, sequences)) < 200:
return self._standart(*sequences)
return self._custom(*sequences)
def similarity(self, *sequences):
return len(self(*sequences))
class RatcliffObershelp(_BaseSimilarity):
"""Ratcliff-Obershelp similarity
This follows the Ratcliff-Obershelp algorithm to derive a similarity
measure:
1. Find the length of the longest common substring in sequences.
2. Recurse on the strings to the left & right of each this substring
in sequences. The base case is a 0 length common substring, in which
case, return 0. Otherwise, return the sum of the current longest
common substring and the left & right recursed sums.
3. Multiply this length by 2 and divide by the sum of the lengths of
sequences.
http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/DDJ/1988/8807/8807c/8807c.htm
https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/ratcliff-obershelp.js
https://xlinux.nist.gov/dads/HTML/ratcliffObershelp.html
"""
def maximum(self, *sequences):
return 1
def _find(self, *sequences):
subseq = LCSStr()(*sequences)
length = len(subseq)
if length == 0:
return 0
before = [s[:s.find(subseq)] for s in sequences]
after = [s[s.find(subseq) + length:] for s in sequences]
return self._find(*before) + length + self._find(*after)
def __call__(self, *sequences):
result = self.quick_answer(*sequences)
if result is not None:
return result
scount = len(sequences) # sequences count
ecount = sum(map(len, sequences)) # elements count
sequences = self._get_sequences(*sequences)
return scount * self._find(*sequences) / ecount
lcsseq = LCSSeq()
lcsstr = LCSStr()
ratcliff_obershelp = RatcliffObershelp()