在 Python 中检查较长字符串中存在的模糊/近似子字符串？

Question

使用像 leveinstein (leveinstein or difflib) 这样的算法，很容易找到近似匹配。例如。

>>> import difflib
>>> difflib.SequenceMatcher(None,"amazing","amaging").ratio()
0.8571428571428571

可以通过根据需要确定阈值来检测模糊匹配。

当前需求：根据更大字符串中的阈值查找模糊子字符串。

例如。

large_string = "thelargemanhatanproject is a great project in themanhattincity"
query_string = "manhattan"
#result = "manhatan","manhattin" and their indexes in large_string

一种蛮力解决方法是生成所有长度为N-1到N+1（或其他匹配长度）的子串，其中N为query_string的长度，对它们一一使用levenstein，看阈值。

python中是否有更好的解决方案，最好是python 2.7中包含的模块，或者外部可用的模块。

---------------------更新和解决方案----

Python 正则表达式模块工作得很好，尽管它比用于模糊子字符串情况的内置 re 模块慢一点，由于额外的操作，这是一个明显的结果。 期望的输出是好的，并且可以很容易地定义对模糊程度的控制。

>>> import regex
>>> input = "Monalisa was painted by Leonrdo da Vinchi"
>>> regex.search(r'\b(leonardo){e<3}\s+(da)\s+(vinci){e<2}\b',input,flags=regex.IGNORECASE)
<regex.Match object; span=(23, 41), match=' Leonrdo da Vinchi', fuzzy_counts=(0, 2, 1)>

Answer 1

我使用fuzzywuzzy基于阈值进行模糊匹配，使用fuzzysearch从匹配中模糊提取单词。

process.extractBests 接受查询、单词列表和截止分数，并返回匹配元组的列表以及高于截止分数的分数。

find_near_matches 获取process.extractBests 的结果并返回单词的开始和结束索引。我使用索引来构建单词并使用构建的单词在大字符串中查找索引。 max_l_dist of find_near_matches 是“Levenshtein 距离”，必须根据需要进行调整。

from fuzzysearch import find_near_matches
from fuzzywuzzy import process

large_string = "thelargemanhatanproject is a great project in themanhattincity"
query_string = "manhattan"

def fuzzy_extract(qs, ls, threshold):
    '''fuzzy matches 'qs' in 'ls' and returns list of 
    tuples of (word,index)
    '''
    for word, _ in process.extractBests(qs, (ls,), score_cutoff=threshold):
        print('word {}'.format(word))
        for match in find_near_matches(qs, word, max_l_dist=1):
            match = word[match.start:match.end]
            print('match {}'.format(match))
            index = ls.find(match)
            yield (match, index)

测试：

query_string = "manhattan"
print('query: {}\nstring: {}'.format(query_string, large_string))
for match,index in fuzzy_extract(query_string, large_string, 70):
    print('match: {}\nindex: {}'.format(match, index))

query_string = "citi"
print('query: {}\nstring: {}'.format(query_string, large_string))
for match,index in fuzzy_extract(query_string, large_string, 30):
    print('match: {}\nindex: {}'.format(match, index))

query_string = "greet"
print('query: {}\nstring: {}'.format(query_string, large_string))
for match,index in fuzzy_extract(query_string, large_string, 30):
    print('match: {}\nindex: {}'.format(match, index))

输出：

query: manhattan  
string: thelargemanhatanproject is a great project in themanhattincity  
match: manhatan  
index: 8  
match: manhattin  
index: 49  

query: citi  
string: thelargemanhatanproject is a great project in themanhattincity  
match: city  
index: 58  

query: greet  
string: thelargemanhatanproject is a great project in themanhattincity  
match: great  
index: 29 

Answer 2

上面的方法很好，但是我需要在很多干草中找到一根小针，最后是这样接近它的：

from difflib import SequenceMatcher as SM
from nltk.util import ngrams
import codecs

needle = "this is the string we want to find"
hay    = "text text lots of text and more and more this string is the one we wanted to find and here is some more and even more still"

needle_length  = len(needle.split())
max_sim_val    = 0
max_sim_string = u""

for ngram in ngrams(hay.split(), needle_length + int(.2*needle_length)):
    hay_ngram = u" ".join(ngram)
    similarity = SM(None, hay_ngram, needle).ratio() 
    if similarity > max_sim_val:
        max_sim_val = similarity
        max_sim_string = hay_ngram

print max_sim_val, max_sim_string

产量：

0.72972972973 this string is the one we wanted to find

Answer 3

即将取代 re 的新正则表达式库包括模糊匹配。

https://pypi.python.org/pypi/regex/

模糊匹配语法看起来相当有表现力，但这会给你一个包含一个或更少插入/添加/删除的匹配。

import regex
regex.match('(amazing){e<=1}', 'amaging')

Answer 4

最近我为 Python 写了一个对齐库：https://github.com/eseraygun/python-alignment

使用它，您可以使用任意评分策略对任何一对序列执行全局和局部比对。实际上，在您的情况下，您需要半本地对齐，因为您不关心 query_string 的子字符串。我在以下代码中使用局部对齐和一些启发式方法模拟了半局部算法，但很容易扩展库以实现正确的实现。

这是为您的案例修改的 README 文件中的示例代码。

from alignment.sequence import Sequence, GAP_ELEMENT
from alignment.vocabulary import Vocabulary
from alignment.sequencealigner import SimpleScoring, LocalSequenceAligner

large_string = "thelargemanhatanproject is a great project in themanhattincity"
query_string = "manhattan"

# Create sequences to be aligned.
a = Sequence(large_string)
b = Sequence(query_string)

# Create a vocabulary and encode the sequences.
v = Vocabulary()
aEncoded = v.encodeSequence(a)
bEncoded = v.encodeSequence(b)

# Create a scoring and align the sequences using local aligner.
scoring = SimpleScoring(1, -1)
aligner = LocalSequenceAligner(scoring, -1, minScore=5)
score, encodeds = aligner.align(aEncoded, bEncoded, backtrace=True)

# Iterate over optimal alignments and print them.
for encoded in encodeds:
    alignment = v.decodeSequenceAlignment(encoded)

    # Simulate a semi-local alignment.
    if len(filter(lambda e: e != GAP_ELEMENT, alignment.second)) != len(b):
        continue
    if alignment.first[0] == GAP_ELEMENT or alignment.first[-1] == GAP_ELEMENT:
        continue
    if alignment.second[0] == GAP_ELEMENT or alignment.second[-1] == GAP_ELEMENT:
        continue

    print alignment
    print 'Alignment score:', alignment.score
    print 'Percent identity:', alignment.percentIdentity()
    print

minScore=5 的输出如下。

m a n h a - t a n
m a n h a t t a n
Alignment score: 7
Percent identity: 88.8888888889

m a n h a t t - i
m a n h a t t a n
Alignment score: 5
Percent identity: 77.7777777778

m a n h a t t i n
m a n h a t t a n
Alignment score: 7
Percent identity: 88.8888888889

如果您删除 minScore 参数，您将只获得得分最高的匹配项。

m a n h a - t a n
m a n h a t t a n
Alignment score: 7
Percent identity: 88.8888888889

m a n h a t t i n
m a n h a t t a n
Alignment score: 7
Percent identity: 88.8888888889

请注意，库中的所有算法都有 O(n * m) 时间复杂度，n 和 m 是序列的长度。

Answer 5

用difflib.SequenceMatcher.get_matching_blocks怎么样？

>>> import difflib
>>> large_string = "thelargemanhatanproject"
>>> query_string = "manhattan"
>>> s = difflib.SequenceMatcher(None, large_string, query_string)
>>> sum(n for i,j,n in s.get_matching_blocks()) / float(len(query_string))
0.8888888888888888

>>> query_string = "banana"
>>> s = difflib.SequenceMatcher(None, large_string, query_string)
>>> sum(n for i,j,n in s.get_matching_blocks()) / float(len(query_string))
0.6666666666666666

---

更新

import difflib

def matches(large_string, query_string, threshold):
    words = large_string.split()
    for word in words:
        s = difflib.SequenceMatcher(None, word, query_string)
        match = ''.join(word[i:i+n] for i, j, n in s.get_matching_blocks() if n)
        if len(match) / float(len(query_string)) >= threshold:
            yield match

large_string = "thelargemanhatanproject is a great project in themanhattincity"
query_string = "manhattan"
print list(matches(large_string, query_string, 0.8))

上面的代码打印：['manhatan', 'manhattn']