这是我想到的:

import string

def match(a,b):
    a,b = a.lower(), b.lower()
    error = 0
    for i in string.ascii_lowercase:
            error += abs(a.count(i) - b.count(i))
    total = len(a) + len(b)
    return (total-error)/total

if __name__ == "__main__":
    print(match("pple inc", "Apple Inc."))

你可以创建这样一个函数:

def similar(w1, w2):
    w1 = w1 + ' ' * (len(w2) - len(w1))
    w2 = w2 + ' ' * (len(w1) - len(w2))
    return sum(1 if i == j else 0 for i, j in zip(w1, w2)) / float(len(w1))

这是内置的。

from difflib import SequenceMatcher

def similar(a, b):
    return SequenceMatcher(None, a, b).ratio()

使用它:

>>> similar("Apple","Appel")
0.8
>>> similar("Apple","Mango")
0.0

如上所述，有许多指标可以定义字符串之间的相似性和距离。我将给出我的5美分，通过展示一个Jaccard与Q-Grams相似的例子和一个编辑距离的例子。

库

from nltk.metrics.distance import jaccard_distance
from nltk.util import ngrams
from nltk.metrics.distance  import edit_distance

Jaccard相似

1-jaccard_distance(set(ngrams('Apple', 2)), set(ngrams('Appel', 2)))

我们得到:

0.33333333333333337

还有苹果和芒果

1-jaccard_distance(set(ngrams('Apple', 2)), set(ngrams('Mango', 2)))

我们得到:

0.0

编辑距离

edit_distance('Apple', 'Appel')

我们得到:

2

最后,

edit_distance('Apple', 'Mango')

我们得到:

5

q - grams上的余弦相似度(q=2)

另一个解决方案是使用textdistance库。我将提供一个余弦相似度的例子

import textdistance
1-textdistance.Cosine(qval=2).distance('Apple', 'Appel')

我们得到:

0.5

还添加了Spacy NLP库;

@profile
def main():
    str1= "Mar 31 09:08:41  The world is beautiful"
    str2= "Mar 31 19:08:42  Beautiful is the world"
    print("NLP Similarity=",nlp(str1).similarity(nlp(str2)))
    print("Diff lib similarity",SequenceMatcher(None, str1, str2).ratio()) 
    print("Jellyfish lib similarity",jellyfish.jaro_distance(str1, str2))

if __name__ == '__main__':

    #python3 -m spacy download en_core_web_sm
    #nlp = spacy.load("en_core_web_sm")
    nlp = spacy.load("en_core_web_md")
    main()

使用Robert Kern的line_profiler运行

kernprof -l -v ./python/loganalysis/testspacy.py

NLP Similarity= 0.9999999821467294
Diff lib similarity 0.5897435897435898
Jellyfish lib similarity 0.8561253561253562

然而，时间的启示

Function: main at line 32

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    32                                           @profile
    33                                           def main():
    34         1          1.0      1.0      0.0      str1= "Mar 31 09:08:41  The world is beautiful"
    35         1          0.0      0.0      0.0      str2= "Mar 31 19:08:42  Beautiful is the world"
    36         1      43248.0  43248.0     99.1      print("NLP Similarity=",nlp(str1).similarity(nlp(str2)))
    37         1        375.0    375.0      0.9      print("Diff lib similarity",SequenceMatcher(None, str1, str2).ratio()) 
    38         1         30.0     30.0      0.1      print("Jellyfish lib similarity",jellyfish.jaro_distance(str1, str2))

内置的SequenceMatcher在大输入时非常慢，下面是如何用diff-match-patch完成的:

from diff_match_patch import diff_match_patch

def compute_similarity_and_diff(text1, text2):
    dmp = diff_match_patch()
    dmp.Diff_Timeout = 0.0
    diff = dmp.diff_main(text1, text2, False)

    # similarity
    common_text = sum([len(txt) for op, txt in diff if op == 0])
    text_length = max(len(text1), len(text2))
    sim = common_text / text_length

    return sim, diff